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Case- Vaginal Foreign Bodies- 10 year old female | OMG: Interesting Cases in Pediatric Radiology
Case- Vaginal Foreign Bodies- 10 year old female | OMG: Interesting Cases in Pediatric Radiology
adhesionanesthesiabodycervixchaptercoronalechogenicflapforeignimageimagesMRIMRI showed a smooth image in upper vaginaNoneobject foundorificeultrasounduppervaginalvaginal adhesion hiding the cervix
Physics of MRI 3: K-Space and Gradients - Part 1
Physics of MRI 3: K-Space and Gradients - Part 1
Is It Cured? MR And Angiographic Imaging After Treatment With Radiopaque Embolizing Agents (Onyx)
Is It Cured? MR And Angiographic Imaging After Treatment With Radiopaque Embolizing Agents (Onyx)
42 vials of EVOHAdditional 17 vials of EVOHan AVPanatomyangiographyarterialAVMavmscoilscontrastcuredembolizationenhancedfeedersferromagneticimageimaginginjectionmissedoccludedoverlayphaseproximalradiopaqueresultshuntingsome coilstemporalTherapeutic / Diagnosticvenousvesselvials
Ethylene Vinyl Alcohol Copolymer In The Treatment Of AVMs: Long-Term Results And Histology
Ethylene Vinyl Alcohol Copolymer In The Treatment Of AVMs: Long-Term Results And Histology
abdominalabnormalamputationangiogramarterialarteriesavmscalfcomponentconsistscopolymerdecreaseddilateddirectdistaldmsoembolicembolizationembolizationsethylenefemoralfollowgraftHigh flow AVMimageimaginginjectionintercostalintramuscularLiquid embolization systemmalformationMedtronicminimalmramultipleonyxpainpatientpatientsphaseportionsprofundaradiationskintranscathetertreatmentultrasoundunderwentvascularveinvenousvinyl
Silent Cerebral Infarcts On CT Or MRI Influence Outcomes Of CEA: How About Outcomes With CAS: Should All Asymptomatic Carotid Stenosis Patients Get A Head CT
Silent Cerebral Infarcts On CT Or MRI Influence Outcomes Of CEA: How About Outcomes With CAS: Should All Asymptomatic Carotid Stenosis Patients Get A Head CT
adjunctiveasymptomaticBothcarotidCarotid artery stentingCarotid endarterectomycerebraldysfunctionendarterectomyincidenceinfarctinfarctioninfarctsipsilateralmicroembolineurologicpatientpatientspostoperativepreoperativepresencerevascularizationrisksilentstenosisstentingstroketermundergoing
MRI Conditional and Off Label Devices - Differences | Scanning Patients with Cardiac Implantable Electronic Devices in MRI
MRI Conditional and Off Label Devices - Differences | Scanning Patients with Cardiac Implantable Electronic Devices in MRI
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What's Next | AVIR CLI Panel
What's Next | AVIR CLI Panel
Radiology and Cardiology - Safety Together | Scanning Patients with Cardiac Implantable Electronic Devices in MRI
Radiology and Cardiology - Safety Together | Scanning Patients with Cardiac Implantable Electronic Devices in MRI
cardiologychapterchecklistconditionsdevicedevicesfull videoimplantinstitutionsmonitoringMRIpatientsradiologyregistryscanscanned
Reveiw of Pacemakers and ICDs | Scanning Patients with Cardiac Implantable Electronic Devices in MRI
Other Non-invasive Ways to Image the Lymphatics  | Lymphatic Imaging & Interventions
Other Non-invasive Ways to Image the Lymphatics | Lymphatic Imaging & Interventions
Physics of MRI 3: K-Space and Gradients - Part 2
Physics of MRI 3: K-Space and Gradients - Part 2
2012acquireacquiringacquisitionchaptercomplicatedconceptcorrespondsdatadeterminedetermineddimensiondimensionaldimensionsdirectiondirectionsdistributionevolvingextendfieldfrequencyfull videofunctiongradientgradientsimageimaginginterpretationlocalizemagneticmultisliceorthogonalphasepositionpulsesequencesignalsinusoidalspatialtimestrajectorytransformtypicallyUHNvariablevariationvarieswater
Physics of MRI 8: Advanced Concepts - Part 2
Physics of MRI 8: Advanced Concepts - Part 2
2012accelerationacquireacquiredacquisitionadditionalamountanatomyapparentartifactsbasedbasicbladebladescenterchaptercoefficientconceptscontrastdatadependingdiffusediffusiondirectionechoeliminateessentiallyfactorfiberfibersfieldfrequencyfull videogenerategeneratesgradientgradientsimageimagesimaginglargerleadlineslossmagneticmicroscopicmoleculemoleculesmotionmoveMRIneuraloppositeparallelphasepolarpropellerproportionalpulsereducerequirerotatescansequencesignaltechniquetissuestrackingtractstransformUHNwaterwaveweighted
Conditional Pacemaker and ICD Timeline | Scanning Patients with Cardiac Implantable Electronic Devices in MRI
Conditional Pacemaker and ICD Timeline | Scanning Patients with Cardiac Implantable Electronic Devices in MRI
chapterelectrophysiologyfull videoMedtronicpacemakerpacemakerspatientsradiologytimeline
Physics of MRI 5: Relaxation and Image Contrast - Part 2a
Physics of MRI 5: Relaxation and Image Contrast - Part 2a
2012acquireacquiredacquisitionadjacentangleassumecenterchaptercompletecontrastcorrespondsechoevolveexamineflipfull videogenerategeneratedgeneratesgradientimageinitialintensityiterationsliesmagneticmechanismsMRImyocardiumparametersplotportionpulserecoverrecoveryrepeatsequencesequencesshortersignalsimplyspinstartingsteadytimetippedtissuetransverseUHN

conserved a treatment did not help and this was just a pre image and that didn't have a post image yet he came back after six months iii VAS and he said why should I do an MRI If I'm really pain free? So this are just a pre-up images I treated those two discs which

review of the electrical conduction pathway here.

We're gonna, this is a slide that just shows how normal conduction would work initiated at the sinoatrial node on down. And then here the yellow color within these diagrams, shows the areas of the heart that are depolarized. And, of course, our patients who

have ended up at the electrophysiologist have some kind of disruption in this conduction. And so, when these patients, when and if these patients end up with a device and end up in radiology we need to have a basic understanding

in order to write a protocol you need a good literature review.

So the basis for our protocol is Dr. Nazarian's study in 2011. I'm gonna refer to some other's here, Dr. Poh, the MagnaSafe Registry, of course look at society recommendations like the ACC, and HRS, and AHA,

and other resources as well. So Dr. Nazarian at Johns Hopkins, along with many others, did a kind of a landmark study in evaluating a protocol for scanning these patients. They used the magnetic strength of 1.5 tesla only.

They did a nonrandomized trial of 555 MRI studies. And they're saying with appropriate precautions with that standardized protocol in place that MRI can be done safely in these patients. I mention Dr. Henry Halperin here, he was also part of that study.

I believe he works with Medtronic. But as I said, there's a big group there. And I'll get back to him in just a moment, as well.

okay all right let's go over another case we have a ten-year-old with smelly

and bloody leukorrhea itching and vulva irritation always want to rule out sexual abuse of course when there's any vaginal discharge or any vaginal bleeding as well okay they're on trans abdominal ultrasound

there was an echogenic image that was noted in the upper part of her vagina and she had a badge on ah Skippy under general anesthesia and what that noted was a vaginal adhesion that was hiding the cervix so they decided to do an MRI

and the MRI showed a smooth image in the upper vagina so here's a couple of our images here on the Left we have a coronal MRI t2-weighted images sequence in the upper part of the vagina they can see a foreign body okay on that janaki

right here on the right you can see an adhesion that's kind of hiding the cervix and there's a flap on the side a flap of tissue that's how that's hiding a small orifice which is leading to the cavity of the foreign body and this is

the foreign body that was found it's a plastic dolls house glass and they suspect that it had been present there for several years okay next case a

Hi, my name is Marshall Sussman, I'm an MRI physicist at the University Health Network and the University of Toronto, I'm giving a series of lectures on basic MRI physics. This lecture is going to be the third in the series, and this one's called k-space in gradients. Now, in the second lecture, I went through the relationship between k-space and image formation MRI, and I showed how we use gradients to move around in k-space and acquire data in MRI. But what

I didn't tell you is how the gradients actually move you around in case phase. So in this third lecture here that is what we're going to get into a little bit more details. And this lecture is divided into two pieces. To give an outline of what's in this lecture, I'm gonna start of by doing just a brief review of some of the physics that we've talked about so far. So I'm gonna again review fourier

transform theory that we talked about in the k-space lecture previously and I'm also gonna give a brief of you of signal generation which we discussed in the very first lecture. Then we're gonna move on and describe how we use gradients to localize spacial position in one dimension. And then we're gonna talk about how gradients and k-space are related in one dimension.

Then I'm gonna move on to talking about how we can localize spacial position in two dimensions. So, let's begin with the brief review of some of the physics concepts. So first of all, fourier transform theory, again, this will just be a repeat of what we covered in the previous lecture. So we know that we can describe a signal as in a time domain, a signal just oscillating as a function

of time. In this case, this signal oscillates at a rate of one cycle per second. So you can see we have this red lining here, to indicate that we've gone through one complete cycle within one second. So, an equivalent way of representing that signal, is in the histogram domain. So on the left here you can see we have now an occurrence of one cycle per second, a frequency of one cycle per second, occurring

exactly once. Here, we have a second signal oscillating at two cycles per second. Again, you can see the red line indicating we go through two complete cycles in one second, and the histogram representation of that signal again a single occurrence at two cycles per second. We can add various different components of those signals together. So if we add these two signals together, we get a composite signal

that looks like this. And in the histogram domain, we get representation that looks like this. So one occurrence at one cycle per second and the second occurrence at two cycles per second. And we can extend that concept and add various different combinations of different frequency components together. So here we have one combination of one cycle per second and two cycles per second. Counts of the

signal you get looks like what you see here. The signal has a function of time, and representation looks like this on the left. So here we have a single current at one cycle per second and we have two occurrences at two cycles per second. And we showed previously how this two are related by a fourier transform which is just a simple mathematical

operation. So we can get one if we just have the signal in one domain, we can determine what the representation signal, the other domain will be, by applying a fourier transform. Second concept I wanna review is the basic concept underlying signal generation. So from the first lecture we saw that we apply on RF pulse and that causes a magnetization to tip away from the direction o f the magnetic

field. Once we do that, magnetization begins to rotate around the direction of the magnetic field, and in turn the spears and an important concept is that the speed of rotation is proportional to the strength of the magnetic field. So the higher the magnetic field the faster the signal rotates. And as a result of that rotation, we have an electrical signal that's

generated. And this is what we ultimately detect as our signal. So, that just is a very brief review of some of the concepts we went over in our first two lectures. So now, we're gonna move on to talking about how we can localize spacial position in one dimension. So, the way we do that, is through the use of gradient. So, we talked about gradients already briefly in some of our previous lectures.

And we saw that gradients are simply magnetic fields that vary linearly with spacial position. And these are magnetic field gradients that are superimposed on top of the main external magnetic field. So just to give an example here, here we have a person lying in the MR scanner. And as a result of the external magnetic field, the magnetic field is uniform everywhere inside that person's body.

If we turn on our magnetic field gradients, then the magnetic field now varies linearly as a function of spacial position. So, we can see that towards the head, the magnetic field is slightly lower than the initial magnetic field and as we go towards the feet, the magnetic field continuously increases till it's on this side, slightly larger than the initial magnetic field. I'm gonna

look at three particular locations. So here we have at the head magnetic field that's less than B zero which is the main initial magnetic field. It's B zero equals to the initial magnetic field at the center, and equal to slightly greater value at the feet. [SOUND] Now, if you recall from the review I just did a few slides ago, remember that I said speed of rotation, of the magnetization is

proportional to the strength of the magnetic field. So in particular what that means, is that this different spacial positions, magnetization is going to be rotating at a different frequency. So in particular the one towards the head is gonna be rotating at the slowest frequency because it has the lowest magnetic field, whereas the one at the feet, is gonna be rotating the fastest because it experiences

the highest magnetic field. Now, another fact which I need to mention here, is that the frequency of the signal that you emit in MRI, is directly proportional to the rotational frequency. So if we are rotating at say 63 megahertz, we made a signal of 63 megahertz. So if we rotate at 64 megahertz, the signal is emitted at 64 megahertz. So what that means is the

frequency of each one of this signals is going to be slightly different, in particular the one at the head because it sees the lowest magnetic field, is gonna have the lowest frequency. The one at the feet because it sees the highest magnetic field, is gonna have the highest spacial frequency. So up until this point, I've been using this figure of the body to represent the

subject that's being imaged in MRI. But the body is is obviously quite a complicated structure, so to simplify the explanation, instead of dealing with the body, I'm gonna actually just describe my contents based on imaging jugs of water. So here we have in this case one jug of water at this position, three jugs of water in the middle position, and two jugs of water on the opposite side. So if we consider

what's going on with the signal, so again we still have the same concept where magnetic field strength varies linearly in space or position. So just as before the signal is gonna be at the lowest frequency here and the middle frequency in the center position and the highest frequency on the right, which is indicated here but now in this case we have three jugs of water here so there's

gonna be three times as much signal at the middle frequency. And the signal on the right, is going to have twice as much signal as the one on the other end emitted at the highest frequency. So this slide here shows you what the overall signal that we're going to receive, when we do our MR experiment with these three jugs of water. So essentially at this stage we can't distinguish between

the signal coming from any of these individual jugs of water. We're just measuring a net signal. So it's just simply the sum of all the signals coming from these different components here. So particular as before have one signal at this frequency here, three signals at the middle frequency and, two signals at the highest frequency. And that will add up to produce this overall composite signal,

because this is what we're gonna measure in our MR experiment. Now this is essentially what I would like to call the forward solution of MRI. What I mean by that is that if we have a known distribution of water, in this case jugs of water, we can then go through the physics behind what's going on, and predict what the signal we get out at the end of the days. But, obviously in a real experiment

that's actually not what we want, it's the inverse of that, because we actually don't know what the distribution of water is. That's what we're actually trying to measure. So we wanna actually get the reverse solution. We wanna say, given this signal here, we measure this signal here. We wanna be able to go backwards and generate the relative distribution of water that

generates this particular signal here. So in the next slide, I'm gonna show you how we do that. So, just to, again, rewrite this same diagram here, we have these different amounts of water using different amounts of signal at different frequencies and we get a composite signal. Now, if you look at this diagram closely, this is actually exactly similar to this fourier transform theory concept

that I described earlier. In the fourier transform example we had different amounts of signal occurring at different frequencies adding up to produce a composite signal. So, in this case we had one signal oscillating leading at the lowest frequency and two signals oscillating at the higher frequency to produce this composite signal here. In my MRI example, I have one jug here producing this lowest

frequency, three jugs producing the middle frequency and two jugs producing a highest frequency producing this composite signal here. So, in the case of the fourier transform, to determine what is the distribution of water, what is the distribution of signals, I applied a fourier transform, and that gave me the underlying distribution of frequency components. So I can do the exact same thing in my MRI case, if

I just simply apply a fourier transform for that signal, that gives me a histogram that tells me the relative distribution of frequencies. In particular it's gonna tell me I have one unit at the lowest frequency, three units of the middle frequency, and two units of the highest frequency. And I can convert this into an image if I simply map the height of this histogram into a grey

scale image. I can actually generate an image of these three jugs of water here. So the black one has the lowest intensity, so the lowest amount of water, the middle one has the highest amount of water, and the far one in the far right has the intermediate amount of water. So just using these basic signal processing concepts in fourier transform,

I can essentially get the reverse solutions. I have this composite signal generated by my MRI data, when I apply a fourier transform, I can then uncover what the underlying distribution of water that produce that signal was. [BLANK_AUDIO] [SOUND] So that illustrates the concept of spacial localization in one n dimension.

Now we're gonna talk a little bit more detail about how gradients and k-space are related in one dimension. So here I have this same example just to recall what we we're using before and just to put some concreteness to this example, I'm gonna use some specific frequency values on these numbers here. Now, these frequency values aren't real, the real MR frequencies are much higher, but just for

the sake of this illustration, these are the ones I'm using. So let's say that this position here the signal is emitted at one hertz, the middle one is emitted at two hertz, and the far one is emitted at three hertz. Mathematical format of these signals are sine [UNKNOWN] So it's just simply the sine of the frequency times time that describes

each of this signals. So this one is sine of one hertz times time, sine of two hertz times time, because it's a frequency of two hertz but now there's three times the signal because there's three jugs of water so we multiply by three, and similar the high one sine of three hertz times time and there's two jugs of water there. So again the conficite signal is just simply the sum of all those

and mathematically the signal is described by this equation here, it's just simply the sum of three components from I equals one to three, because we have three different locations here. The A here, is the relative amount of each of these signals present so A at this position is 1, a at this position is 3 and a at this position

is 2, times the sine of the frequency, and obviously that also varies at each one of these positions. So the A, is what we're ultimately interested in, because that's really tells us the distribution of water, the relative amount of water at each position. And the frequency, we know that the frequency is proportional to the strength of the magnetic field. And we further know that the strength of

the magnetic field, is equal to the gradient times the spacial position, because that's the linear magnetic field gradient, plus the static magnetic field, which is what which always exist. Now, just to simplify the Math since the static magnetic field of this bizia/g magnetic field is the same at all spacial positions, I'm gonna ignore that, so I'm gonna get rid of that from this term here.

So now let's write down what the signal looks like making the substitutions. So here I have my signal as this, and my frequency is going to be replaced now by, the gradient times the spacial position. So if I do that, my signal now takes on this Mathematical form here. Now, the key to this whole concept of k-space is that we make this substitution here. We define this variable

and we call it K equal to the gradient times the time. [BLANK_AUDIO] With that substitution, the signal B takes on this format on the bottom here. So the signal is the sum of the distribution of water which is the same as before. Now it's a sine of this K variable times X, the special position. So now how can we interpret

the signal? So again here's my initial format, so we have the signal of all means and it's function of time before I place in my k-space variable. So if you look at the signal oscillating is a function of time it just simply appears like this. If we make this case substitution, we can equivalently consider the signal to evolve as a function of the K variable so instead of

the signal evolving as a signal of time, we now have the signal evolving as a function of this K variable from zero to same X value. And this really discourages the whole K concept of why when we turn on the gradients we move through k-space because essentially, if we don't have any gradients on we're essentially starting at zero position. As soon as we turn this gradients on

we're essentially moving through k-space, so this K variable is evolving so this is why the gradients allow you to move through k-space and this is really the the piece that we were missing in the first [UNKNOWN] So K is just directly proportional to the gradients. So as long as there are gradients on, we are moving through k-space. So equivalent that you can think of as we're moving through

the k-space, we're essentially mapping out these grey scale values that you see here. So I've just converted this signal loss in this function of time to simply a grey scale intensity and that's the more familiar form of k-space that we see. [BLANK_AUDIO]

So as the signal evolves the function of time, this is essentially filling up the k-space data matrix. [BLANK_AUDIO] On one hand it seems like sort of a simple almost a trivial substitution. Okay I've just replaced this K with a [UNKNOWN]

gradient times time with this variable K. But as I'm gonna show you in this slide here, it does have some pretty important ramifications, because in particular what it tells us, is that it doesn't really matter what our gradients is doing, as long as we cover the same region of k-space, then the data that we acquire is gonna be exactly the same in both cases. And that's how we can get to different

k-space trajectories. So let's start out with this first example here, we have a gradient that's a constant gradient on as a function of time. So this is what the gradient's doing on the left, and on the right, I'm going to show you what goes on in k-space. So if we turn our gradient as a function of time, our k-space simply evolves from its initial zero position

because remember we always start at zero position k-space out to some maximum k-space value. And the speed that we're going at is just simply proportional to the strength of the gradient. So now let's have a second example here where turn a gradient on for a period of time, turn it off, and then turn it back on. So what's going on in k-space? So in k-space we're simply have the

gradients on, we are acquiring data just like in the first case. But now because we've stopped, we're not doing anything. So we just sit there at that same k-space position. We then resume and we turn the gradient back on and then we resume form that same k-space position. So again these are two very different gradient wave forms, but in fact we still cover exactly the same k-space

region. Again we just have to go back to the substitution here to see what exactly is going on. And as I mentioned in previous lectures, in general infinite numbers of k-space trajectories are possible. So I could, cover this exact same region of k-space by having essentially an infinite number of different ways of doing this. So turn it

on and off 3 times, 4 times you know a 1000 times, it wouldn't matter as long as I'm covering the same ultimate region of k-space, the image I generate will be exactly the same. And to say I just to [INAUDIBLE] that point in, the ultimate image you get doesn't matter how you covered k-space, it just matters what k-space data you've acquired.

[BLANK_AUDIO] Now, let's go a little bit deeper into what's going on with k-space. So the gradient is just simply a linear magnetic field gradient. So if we have this initial gradient we're training on here, we cover k-space in the manner that I just showed you. If I increase the strength of the gradient that means that I'm increasing this

g-value. So I'm increasing the slope of this magnetic field. So the magnetic field changes more rapidly as a functional spacial position. So how does that affect how we go through k-space? Well it just simply speeds up how we go through k-space. If this gradient is larger, that means we're gonna go through K faster. So in particular if I have a gradient that's

twice as large, but only half as long, again going back to this k-space substitution, here you'd see that that just simply means I'm gonna go through k-space twice as first. Again I'm covering the same region of k-space so when I reconstruct the image it's gonna look exactly the same. But I've now gone through k-space, I've acquired my data twice as fast. Similarly, if I invert the

polarity of the gradient, that just simply adds a minus sign onto this G. And again looking at the k-space variable you can probably anticipate what this is gonna do to how we go through k-space it's just simply going to invert the direction that we go through k-space. So instead of going right to left, we're gonna be going left to right. So we just fill up our k-space in exactly the

opposite direction. Now, we're gonna look into some issues related to gradients and image reconstruction. So as we've said many times now the images are reconstructed by fourier transforming the k-space data. So we acquire a series of k-space data and we apply a fourier transform on that and we get in this case the distribution or the relative distribution of order

that's generating that signal. So what's happening mathematically? So we saw that we can consider the signal evolving as a function of K, and that's indicated by this formula here. So if we apply a fourier transform to this signal as a function of K, that's applying a fourier transform to the right hand side of this equation. Now again I'm not gonna go through the Math to derive this, but I'm just gonna

tell you the result that fourier transforming this equation gives you this A variable here. And as we recall the A, is the relative distribution of the water. So this is actually the A of the image that we are ultimately interested. So this image or this distribution of water and the k-space signal data are often referred to as fourier transform pairs. So signal is a function

of K, or image is a function of spacial position, are related to each other via fourier transform, that's why they're called fourier transform pairs. So now we are gonna go into a little bit more detail, on to how the properties of k-space affect the ultimate properties of your image. So the first thing we are gonna talk about is how k-space is related to the ultimate spacial resolution

of your image. So we call again before k-space position is equal to gradient times time. So we turn our gradient on for a period of time, we go through k-space as before. Now, one question is how long do we acquire the data force. So I cut off my data acquisition at this point here but there is nothing special of this particular

point in time. Why couldn't I have expended it further we're going short. Determines how long we acquire the data acquisition for. But it turns out that the special resolution of an image is inversely proportional to the maximum k-space value you acquire. So if

we go out further in k-space, we generate an image of higher and higher spacial resolution. In the previous k-space lecture we saw why that a wide as you go to higher and higher k-space values this gives you more and more fine detail. But mathematically this is what's going on. So the spacial resolution of image, is inversely proportional to how far you go out in k-space. So for example lets say

this first example here where I go up to this particular region of k-space, lets say this provides me with a spacial resolution of one millimeter so the image has a spacial resolution of one millimeter. Finally go out half this far in k-space, another way is I only acquire data for half as long, and what's going to happen to my resolution, is it's going to get worse by a factor of two. So I'll

go only half as far in k-space and my resolution is now courser, instead of one millimeter It's now two millimeters. So again, if you want to determine how far we need to go out in k-space we just simply have to determine what is the spacial resolution you want to get, what is our gradient, and that tells us how long we have to acquire the data for. Another

important property of imaging is the field of view. So that's how big of a region are we going to be imaging with MR. So again here is our initial gradient, it's function of time and is the initial k-space region we cover. Now, the MR signal that we're generating from the body, is generated continuously. So continuously is a function of time. But in practice since we can't store an infinite

amount of data, the signal that we actually measure is only sampled discretely. So we only measure it at the discrete distributional points like you see on the image on the right. So what determines how finely we sample that? Well, it turns out the field of view of our imag,e is inversely proportional to how finely these are sampled, so in other words the larger spacing

in between these k-space points, the smaller our field a view. And in turn we know that K is equal to the gradient times time, so essentially the spacing is determined by how finely or how quickly we sample our data which is this Delta T here. So on most scanners, it's the sampling rate that's controlled explicitly through the bandwidth. So as an example let's say that we specify a bandwidth

of 32 kilohertz, that's 32,000 samples per second. That means that in turn, we have a sample rate of 1 over 32,000 or in other words, 31 microseconds per sample. We're basically sampling our data every 31 microseconds. So as I said on most scanners it's this bandwidth that's controlled explicitly. So we prescribe a particular field of view, we prescribe a particular bandwidth and then

the scanner we're going to calculate what gradient we need, in order to generate the appropriate k-space spacing, to produce the field of view that we've specified. So that's going to end the first part of this lecture, so we've basically done a brief review of the concepts of the fourier transform signal generation, and we talked talked about how we can use gradients to localize

spacial position in one dimension. So in the next lecture I'm gonna move on and talk about how we can extend this concept to localized spacial positioning in two or even more dimensions. [BLANK_AUDIO]

And this is just a picture of that large consensus statement.

Okay, so Dr. Poh, he and his group in Singapore also did a large study reviewing the dangers and the difficulties in MRIs and MR scanning and attempts to improve safety. They gave a lot of information about the various manufacturers and how

each of them roll out a safe protocol. So their conclusion is that because of the increasing number of patients with CIEDs, radiologists should be able to utilize MRI in this population. So from his study, this just gives a little

insight into what the manufacturers have done to increase safety in how they've created these devices. Each of these devices has an MRI mode here, and even the way some of these sensors have been designed there's an older design that has been taken out called a reed switch

and they've changed that to that Hall effect. And a lot of this does relate to the reduction in heat generation that can happen, both from the gradient coil and then through the radio frequency pulses that happen in MRI. You can also see here,

can you guys see that? You can't see it at all. Oh my, okay, sorry about that. But you can see in there that the way they've been created is to minimize the absorption rates

and whatnot to prevent the heating. This shows you here, I had mentioned that in our protocol patients must have a chest x-ray, this is what these devices look like on chest x-ray so they can be identified

and each of them, or most of them, have a space on there that shows that it is MR-conditional on both the pulse generator and the lead markers. Again, is a good place to start to find out if your

patient's device is MRI-conditional.

- Is it cured or not if you have a radiopaque embolization agent, and you are doing some x-rays, there may be problems. How to go forward, it's not the mouse here? Maybe this, this one? Ah, I see. Okay, what are radiopaque embolization agents?

Basically, you have glue, NBCA and EVOH, like Onyx, Squid, or PHIL. And they, all of them are radiopaque and they are liquid, and they can overlay the result of your embolization. And we start with MR-Angiography and the most important basic fact here

is that you have to use time resolved, contrast enhanced MR imaging. It's not worthwhile to do this with time of flight or phase contrast or anything without contrast because you need that contrast and you need a high temporal resolution

meaning many, many sequences as a follow-up. And you need a large field of view, because after treatment, there can be recruiting feeders from very remote sites. And how do you image the result of an embolization in DSA in contrast angiography?

You know this old thing with high, hot, helluva lot? Of course you need a very proximal injection site. You need a high injection rate and you need enough contrast, otherwise you will say, okay, this AVM is cured, but it isn't. And the most important thing is,

don't show me any arterial phase AVM images and tell me that you cured this AVM. You need to go to a late parenchymal phase or most important, you need to go to a venous phase. And if you see any early venous shunting, this AVM is not cured.

So you're overestimating the result of your embolization. So here's an example. This is a huge abdominal AVM and this is, you have to image high, because you see a lot of arterial feeders there and even see the inferior mesenteric artery

contributing to this AVM and you see where the arrow is, there is the pigtail catheter up there. Did I miss a feeder, yes. I missed a feeder, you have to go very high up, it was even feeding through the renal arteries, this AVM. So never underestimate a big AVM.

So what are the special issues with DSA imaging in AVMs? First of all, it's a flow-passive thing. Remaining where the point of injection and it's like blood contrast. It will flow through the point of the least resistance, but it will never show you the whole extent of the AVM.

So you really have to inject very proximal with a high flow rate, and if you control your results, you have to wait and to do and a real follow-up is not at the end of the embolization session. You always have to do it later because after extensive embolization session,

like it does, it takes hours. You always will have vessel spasm, and you will have a thrombotic component which shows you an occluded vessel. But wait, a couple of days, it will always recanalize. We're dealing with AVMs here.

So DSA imaging in AVMs is always overestimating your result because you have this vessel spams and you have this thrombosis. And of course, if you have a radiopaque embolization agent, you can have some obscuring overlaying effect,

so this can be a problem as well, but it's not that much of a problem, because you're not looking at the, sort of nidus, you're looking is there any early venous return. And if there is any venous early shunting, the AVM is not occluded.

So here's a, that's a huge, with an AVM in this ten year old boy. You can see the heart, it's showing a grade four AVM. And this is after 42 vials of Onyx in this patient. And some outflow coils, you see them down there. And that's the venous phase DSA.

Is it occluded or not? It's very difficult. I thought maybe it's occluded, but I was not sure. And I did an MR-angio two weeks later, and as you see, it's not occluded. But the DSA, I wasn't very sure.

In the MRA, I am sure it's not occluded, so you have to add 17 more vials there. You see the DSA, is it occluded or not? You don't see any venous return, it was occluded. MR angiography is clearly showing you a result in this patient.

So why is MR angiography better than DSA in controlling the result of your embolization? Of course, it's all this blah blah, noninvasive, but you have no issues with late phase venous imaging because you can image as long as you want.

As compared to DSA, where it costs you some radiation and things like that. The field of view is normally in a good MR scanner. It's bigger than you have available in your angio suite. So that's better for even very distant feeders. And of course, you don't have any overlay

with radiopaque embolization material here. So, from my point of view, DSA is nice, and MRA is better to control the result of AVM embolization. Thank you. (audience applauds)

- [Moderator] Any questions? I thought your examples, yes? - [Bob] No, no, go ahead. - [Moderator] I thought your examples were very good at showing the MR occlusion. And those were in the type 3A, 3B,

AVMs with venous aneurysms, which are large. All the type four infiltrator forms, they can be missed on MR. Type 2A nidal forms can be missed on MR. And direct fistulas might be missed on MR depending on size. What is your opinion of that?

- [Walter] I think if we are talking about high temporal, spacial resolution, dynamic, contrast enhanced MR angiography. When all the rest doesn't matter, it's a waste of time to do a TOF or a phase contrast. And you always see if you have a high temporal resolution,

like five seconds, four seconds, you can even go to three seconds. You always see the early return, so you can't overlook any AVM, but it the fistula or be it a very small, that was formerly known as small vessel type AVM

and things like that, you can't overlook it, because you see the early return. Maybe you don't have the spacial resolution to see very tiny vessels, but you see there is an AVM, and you can't overlook it. If you use this technique.

- [Moderator] Bob? - [Bob] No, that was my question as well, the various types and so on with these radiopaque agents. - [Walter] Yeah, but it's not for classification. My talk was about follow-ups, so... - [Moderator] Well, that's what we're talking too,

follow-up to see if it's there or not. - [Audience Member] I have one question, when you're looking at MR in a setting like this, where coils are present, presumably these are all ferromagnetic and are going to cause all sorts of artifact,

how do you deal with that? - [Walter] Well, you know, most of the coils, nowadays, the coils are not any ferromagnetic coils. Of course, they make some issues with local artifacts, but to see whether there is some AVM left or not,

it's totally unimportant. Of course, you have in gradient echo sequences, you have local artifact with ferromagnetic material. Again, with normal coils, some signal loss. But if you don't want to see

the subtle anatomy of the AVM, you want to see if there an AVM left, is there shunting left or not, so there is no problem. - [Moderator] Well, it is a problem, being that with the focal artifact,

even on platinum or other coils that have minimal effect, within it, you cannot see anything. - [Walter] Yes, but I don't want to see the angi anatomy of tiny residual nidus, I want to see whether there is shunting left or not. And if there is no shunting left, the AVM is gone.

- [Moderator] Thank you very much.

Okay, big step three is communication. This kind of involves networking. I did mention Dr. Halperin who was a part of that landmark study, one of our physicians met him

at a conference and then they continued conversation by email and whatnot as we were writing this protocol. So just to say the stakeholders that are involved they may want to communicate others outside of their own institution.

Plus as a protocol is created, you're gonna have to have ongoing communication within your institution to work out all of these different factors of scheduling and staffing and whatnot. Protocols must be written and revised,

continuing to revise budgeting of time, space, finances. And step four, implementing that protocol. Standardizing what happens with each patient every time. Utilizing a checklist method is how we have done it so far.

And then, evaluating that process.

So this is step two, and I kind of broke this down into 2A and 2B. Just what's foundational is to have that essential MRI safety knowledge to begin with that.

Whatever kind of safety protocols you have at your institution. We have a electronic lesson plan that every nurse, technologist, every staff person has to go through when they come into the MRI department.

The ARIN website is a great resource. There's actually a current presentation, it's a three part presentation on there that I'll refer to in a little bit, specifically for MRI safety, and the societies

are also good places to start as a nurse. Here's an example from our lessons of MRI safety. And I know this is a bit of a review for radiology nurses. This is the MRI safety levels that I was speaking about

that are new on the ARIN website. This is a joint project with SMRT. The safety level one is great for every radiology nurse. The safety level two presentation is geared more towards those

who are making those safety decisions of who can have an MRI.

- I'm going to talk about the use ethylene vinyl alcohol copolymer in the treatment of high-flow arteriovenous malformations. I'm going to describe our long-term results and some of our histology. I have no financial disclosures. The DMSO, the Onyx liquid embolic system,

as you know consists of ethylene vinyl alcohol copolymer suspended in DMSO solvent and mixed with tantalum powder. The polymer will precipitate upon contact with aqueous solutions, such as blood. The study population I'm going to describe consists of 38 patients with high-flow AVMs.

Their demographics are shown here. The mean age of the patients was about 28.9 years with a range of less than a year to 67 years. 16 of these patients had been previously treated with other embolic or sclerosing agents before Onyx. All patients were symptomatic at time of treatment,

all underwent MRI MRA prior to treatment, and ultrasound was performed as needed pre-treatment to assess legion visibility during treatment. We used Onyx 18 and or Onyx 34. The Onyx was delivered by subselective arterial or venous microcatheter techniques.

It was also delivered by a direct injection with ultrasound and fluoroscopic guidance with either tourniquet or manual compression. In any patients that went to operation, histologic examination of tissue explants was obtained. The distribution of the AVMs is shown here.

There was a preponderance in the lower extremity but they were distributed throughout the body excluding the head. The delivery routes, 24 patients received the Onyx via transcatheter delivery, five received direct injection under image guidance and in nine,

a combination of transcatheter and direct injection techniques were used. Our technical success rate was 97%. We were able to get the Onyx to where we wanted to get it. Our clinical success rate was 89%. This was defined as decreased pain

at the six to eight week follow up visit. Unfortunately, not in all patients was the pain response persistent. Four of our patients with diffused AVMs went on to eventual amputation. Our mean follow up in this population was 56 months

with a range of one to 348 months. Our mean follow up following their last Onyx injection was 26 months with a range of one to 124 months. One patient in this group was lost to follow up. Complications occurred in four patients. We had two patients who developed skin ulceration,

one of which required a skin graft and this graft did yield. One person presented to us with a pre-existing radiation burn and his radiation burn got worse during our treatment period but it has subsequently healed but required hyperbaric treatment.

In one newborn patient on Onyx filament from a high-flow AVM in the liver migrated through patent doctospinosis as a strand persisting in the right atrium. I'll just show you two selected cases. This person presented at age 17 following rib trauma,

he was diagnosed with a high-flow AVM. He underwent a series of 19 embolizations beginning in 1983 with a variety of agents being used including alcohol. His first Onyx treatment was in 2013 at age 50 and he underwent a total of five Onyx treatments.

This MRA, oops, sorry, shows the component in the abdominal wall and this other image shows the plural component. The MRA angiogram shows multiple dilated intercostal arteries and recall, these have been treated previously.

The venous phase of the MRA shows this large venous conglomeration down here in the right lower abdominal wall. This is his final Onyx embolization and you can see with direct injection, we have been able to fill the interstices

of this large venous conglomerate. On the arteriogram you can see that we have Onyx in multiple intercostal arteries and we have a coil here to treat the aneurism. We sometimes use the Onyx in conjunction with coils. Here is his final MRA taken two years ago.

You can see we still have that plural component, we've been somewhat reluctant to go deep into that, but the venous component along the lateral abdominal wall has resolved and we can see that we still have some intercostals that are slightly enlarged but the overall number of intercostal arteries

is significantly decreased. Our second patient is a patient who presented at age four with a left calf mass, limp, and pain. He underwent surgical resection of the left medial gastrocnemius muscle, and pathology diagnosis at that time

was an intramuscular hemangioma. He did well for five years but then at age eleven, his symptoms recurred and he was referred to us for work up. His baseline MRA shows a large hypervascular complex malformation in the distal fi. It extends across the joint and we see it

in the proximal calf in the mid calf. The cross-sectional imaging on the MR shows the extensive intramuscular involvement of this legion. This is in the thigh and here we are down in the calf. Our initial angiogram on him showed several components.

This portion was supplied by the profunda femoral artery. We have another component of the malformation supplied by distal branches of the SFA and here in the calf we can see some of the trifurcation, and down in the mid calf. He underwent his first embolization in 2005

and we used embospheres and alcohol. We had minimal response with the alcohol. Over the next year he underwent a series of nine embolizations with Onyx. And here is his final MRA. His problem was significant pain and difficulty ambulating.

You can see on the arterial phase of this time resolved image that we have significantly decreased the arterial in flow of the malformation, however on the venous phase of the MRA, you can see that we still have these aneurysmally dilated veins draining into

the deep femoral vein of the thigh. This is the cross sectional imaging post Onyx embolization just before amputation and you can see, there has been a reduction in the intramuscular involvement but we still have dilated venous channels which we saw on the earlier study.

He underwent amputation in 2008 and you can see that the residual Onyx is there in profunda distribution. His histology is shown here. In some portions of the malformation, we had this abnormal area with multiple small abnormal vascular spaces,

which looked quote more hemangiomatous in type even though we don't prefer to use that word. And here on the trichrome stain, you can see that we have these small abnormal vascular channels. In other portions of the malformation however

we had the more typical appearance of a high-flow AVM with these very thick walled abnormal arterial structures where it's difficult to distinguish where the artery is and where the vein is. Here we have the intraarterial Onyx, producing a cast in the vessel.

On a higher power image, you can see that there are some giant cells interspersed around the Onyx, however this particular case had more giant cells than we typically see in our other specimens. There are some limitations to the use of Onyx.

The first and biggest problem is getting it to flow distally into the nidus. And in order to accomplish that, what we do is we dilute our Onyx 18 with additional DMSO to make it more runny and to make it easier for it to traverse distally.

We agree that the radiopacity of the Onyx can sometimes obscure the anatomy on subsequent treatments however we can usually get around that using oblique injections and using an embo roadmap. The advantages of Onyx are it's a permanent embolic agent. It's associated with minimal post procedure pain

and minimal skin changes. In our experience, Onyx is a safe, durable agent for the treatment of high-flow AVMs. It's associated with minimal post procedure pain, has a low complication rate. It's an effective palliative therapy for large lesions

and it a useful presurgical adjunct. Thank you.

- [Gianluca] Thank you, Mr. Chairman. Thanks to Frank (mumbles) for the very kind invitation. I have nothing to disclose regarding this presentation and silent cerebral infarct is a small, radiologically detected infarction with no history of acute neurological dysfunction given by the lesion.

And they are usually associated with the degree of carotid stenosis, the number of microemboli at TCD and the plaque histology. In the general population, they affect almost 20% of the population

and they are significantly associated with early stroke and long term stroke rate. For that reason, the presence of silent cerebral infarcts is considered an adjunctive risk in patients that undergoing carotid endarterectomy.

And this was already demonstrated by this work by Cao, in which patient with the presence of silent cerebral infarct had a higher risk of postoperative stroke and long term stroke incidence after carotid endarterectomy.

When we looked at a series of patients, both symptomatic and asymptomatic, after carotid endarterectomy. We saw that the presence of brain infarcts was one of the carotid risks that associated with increased stroke rate after endarterectomy,

no matter where the infarct was located, as you can see from this table. So for that reason, we further expanded our analysis to only asymptomatic patients admitted to carotid endarterectomy with this work. And again we saw that silent cerebral infarcts

are significantly associated with early postoperative stroke and that was an independent factor of postoperative stroke in this series of patient. And this effect was sustained at long term either for ipsilateral stroke and combined stroke and death.

And again, this was an independent risk factor of long term stroke rate after carotid endarterectomy. What about carotid stenting? Actually, the literature, there are no paper dealing specifically with this topic.

So we made an analysis on 420 consecutive carotid stenting patient treated for asymptomatic stenosis and all those patients were evaluated with a preoperative CT scan. And if you look at this graph,

you see that there was no difference in patient submitted to stenting with or without the present of silent cerebral infarct. If we look at the two groups of patient, patient with positive presence, the presence of carotid lesion

before endarterectomy and stenting, there was no difference in the two groups. Whereas, if we look at the patient with no evidence of infarction before endarterectomy or stenting, there was a trend toward greater incidence of stroke

in patient undergoing stenting. So, in conclusion, silent cerebral infarcts increase the risk of postoperative events after carotid endarterectomy and this risk should be considered in indication to revascularization of our patient.

In the stenting group, this effect is less pronounced, but this is probably due to the higher overall risk of neurologic event after stenting. In conclusion, to answer to the answer at the beginning, should all asymptomatic carotid stenosis patients

get head CT scan? The question is definitely yes. Thank you.

We could spend a lot of time on this, too, but just to give you kind of a quick idea. When we speak of the MRI-conditional devices, we're talking about those that a specified

MRI environment within certain conditions of use does not pose a known hazard. So probably all of you are aware that devices themselves are labeled as MRI-safe, MRI-conditional, or MRI-unsafe. So we're really only talking about

those that are MRI-conditional, and the conditions can involve many things. That second bullet under MRI-conditional speaks of some of those that does relate to the conditions that the radiologist and radiology technologists

will have to take into consideration. It's also gonna include the leads and the generator combinations. This can vary among manufacturers. So for us, we have to know the implant manufacturer, model, and implant date.

We keep a list of the manufacturers and reach out to them if needed. And as I said before, these are FDA approved. Now on our off-label protocol, on that side, we're speaking about all other CIED systems.

So these could have an MR-conditional generator, but they've been combined with a non-conditional lead, and all kinds of variations along that line. For these patients, they also have to be interrogated within three months of the proposed MRI scan date.

These have to be screened and approved by the EP team. A chest x-ray is required for these patients. And just of note, they are not able to be reimbursed yet through Medicare and Medicaid. So one way the institutions have assisted with that is through registries,

the MagnaSafe Registry is a large one that was done. Right now, our institution at NYU does not have that registry capability, so that may be a scheduling or billing conversation if your institution is considering that.

There is some research out there from some cardiologists, this is by Dr. Do, he's actually here at UCLA,

where he's put out some studies of why cardiologists should choose to implant MRI-conditional devices over the off-label devices. And you can read through this, it makes quite a lot of sense

from my perspective if the patient is requesting it, if they're a young patient, if they already have musculoskeletal problems, if they have malignancies, neurologic disorders, so that's kind of another take on it from the cardiology world.

after having these two cases one in our institution and one at University of North Carolina Chapel Hill that we would then basically upsize our particles to

100 micron and we have not seen that and we're doing a second clinical study and I'm not seeing that as either we had about a 70% reduction in pain so if you look at our visual analog score out to six months and if you look at our

disability it actually paralleled this exactly which is pretty impressive considering mostly patients had bilateral knee pain so out to six months very good results 90% of patients were responders so two

out of our twenty patients did not really respond one patient didn't respond at his one-month follow-up but did respond at his three and six so I still consider him a clinical failure because we expect

these patients to respond by one month here's just an example of a baseline MRI before and after and you can see all that joint effusion there the white that decreases just even after a month how much it decreases and we looked at this

in terms of synovial thickness and distension and even on MRI you can object objectively count calculate synovitis scores and we calculated that they actually statistically decreased this is another patient on the left the

image shows diffuse white enhancement if you will of the synovium of the lining on the right it shows the fluid this is an image just of embolization and I show this image because it's really shocking and this is actually one of our nurses

who's enrolled in a clinical study is this is before this is all we did we embolized the medial aspect of the knee this is one month later 30 days in fact somebody just asked me this when I was in the booth over at the meeting across

the street and basically I said listen I don't know why this happened so quickly I have no idea we didn't tap renu-it into anything else if you look at this premium post it's pretty dramatic so clearly there's an inflammatory process

that we are arresting or stopping in such a short period of time so is there a future for this I don't know it may just we may just fall down and find out that there really is in a great future but so far we know it's at least

technically successful it's the results are positive in the short term long term we're not so sure yet we do need to better understand these risks and I think in my opinion in the long term it'll probably be really really good for

this 40 to 65 year old patient population who's not yet ready for knee replacement surgery this is the algorithm for our clinical study which were almost done enrolling right now it's a randomized control study against

placebo so it's two to one randomization which means one third of the patients actually get a sham procedure so we do an angiogram on their leg they're asleep they have no idea for embolizing they're genetical it arteries or not we wake

them up I think about the table and we follow them up if they're no better they're allowed to cross over and get the treatment the other 2/3 of the

And we're gonna add to that the EP and pacing concepts. So your institution may have

great resources for learning. We utilize the Medtronic Academy online. We use the Radiology SureScan MRI Training, there's so much available on their website. And the Basic Pacing Concepts is a great PowerPoint to start on,

I'll show you that as well. All of these manufacturers are also really wanting all of us to be well-educated so they're willing, quite oftentimes to come to your institution and do training there as well.

This is that Basic Pacing Concepts PowerPoint that I referred to. All of this is available, free of charge, you just sign up with your email and password, and you have all of this available to you.

Okay, so again, radiology and cardiology need to be working together, collaborating.

As we talked about, the stakeholders in both of those services need to buy into this idea that it's good for the organization, good for the patients. As that third bullet there,

securing appropriate staffing does seem to be a road block across institutions. That was partly our experience as well. Who was gonna be monitoring them, when can this happen where the EP team can program the device and reprogram the device?

The radiology nurse is going to be monitoring them in between, when is the magnet available and the radiology technologist available? So all of that has to come together beautifully and it can.

All that sharing of resources between disciplines is key. And really, the people who can make those decisions and make it happen have to really consider how it can benefit their discipline.

Radiology can offer this service to patients where potentially in the past we've always had to have that roadblock there that they couldn't have it, and cardiology can now say, that their patients with these devices

can get the imaging that these other referrers want and potentially that the cardiologist wants, as well. We really talked a lot about what's the best location for scanning these patients and for monitoring these patients.

Our radiology staff wanted to keep these patients at our inpatient site, mainly due to access to resources of personnel and emergency equipment. I think it's a good idea, especially if you're the nurse helping to drive this ship,

is to make a checklist of which personnel are needed. Make a checklist of which emergency equipment is available and how close it is to the MRI scanner. I do think nurses are great at planning of plan B, plan C, so we can help

plan for worst case scenarios. And then, make sure those conditions are in place before you move forward with the plans. And, of course, you want to get all that written up into your protocol, so it is standardized and everybody

must do it every single time. Okay, so this kind of goes back to our values, right? We could spend a lot of time on this, too, but just to give you kind of a quick idea. When we speak of the MRI-conditional devices, we're talking about those that a specified

MRI environment within certain conditions of use does not pose a known hazard. So probably all of you are aware that devices themselves are labeled as MRI-safe, MRI-conditional, or MRI-unsafe. So we're really only talking about

those that are MRI-conditional, and the conditions can involve many things. That second bullet under MRI-conditional speaks of some of those that does relate to the conditions that the radiologist and radiology technologists

will have to take into consideration. It's also gonna include the leads and the generator combinations. This can vary among manufacturers. So for us, we have to know the implant manufacturer, model, and implant date.

We keep a list of the manufacturers and reach out to them if needed. And as I said before, these are FDA approved. Now on our off-label protocol, on that side, we're speaking about all other CIED systems.

So these could have an MR-conditional generator, but they've been combined with a non-conditional lead, and all kinds of variations along that line. For these patients, they also have to be interrogated within three months of the proposed MRI scan date.

These have to be screened and approved by the EP team. A chest x-ray is required for these patients. And just of note, they are not able to be reimbursed yet through Medicare and Medicaid. So one way the institutions have assisted with that is through registries,

the MagnaSafe Registry is a large one that was done. Right now, our institution at NYU does not have that registry capability, so that may be a scheduling or billing conversation if your institution is considering that. And our patients that are scanned

with the off-label device have to sign a separate consent form. Okay, so could institutions or should institutions be implementing these conditional protocols as soon as possible? This is a question of providing safe,

up to date care for patients with pacemakers and defibrillators. It can be considered a concern of access and a barrier to care. Just to give an idea if you think about where we are in New York, New Jersey,

Connecticut, Pennsylvania, that whole area there's only three centers who currently scan patients with the MRI-conditional devices. So, before we were doing it, they were 200 miles away from each other,

so now there are three centers. So there's a lot, millions of people, within that area and certainly people who have been on hold up until this point. I'm gonna speak a little bit about Dr. Nazarian and his research

and he is at Johns Hopkins Hospital. And on their website they're stating, more than 300 patients have been scanned safely and now we consider it the standard of care. Dr. Robert Donnino who helped me out with part of this project,

he is also in agreement at NYU we want to give this care to our patients.

So our scanner involves all those magnets and coils including the gradient and the radio frequency coil. As we know, that MRI is a great tool

for a lot of reasons, specifically soft tissue and bony abnormalities. The brain, spinal cord, nerves, muscles, ligaments, tendons all of these are seen much more clearly on MRI. So there are a lot of reasons

that our patients and our referring physicians are gonna want MRIs on their patients with electronic devices. So there are a lot of benefits but there are also risks with MRIs. Not to go over these in too much detail,

but we're gonna hangout on the implants and the risks involved there. But certainly, the nerve stimulation and some of these other reasons need to be considered as well in this group of patients.

So if you're starting out as a radiology nurse, you have that essential MRI knowledge. Hopefully you have safety screenings in place like this. Here's an example of our screening forms, contrast forms are also utilized as well, is another area, is another good resource if you need to beef up those safety measures.

This is just taken out of that

PowerPoint presentation, just to kind of show that we all need that review of the science behind these pacemakers and ICDs. Just this bottom area here, the voltage, current, and impedance

involved in this equation of Ohm's law that's gonna be the rationale for the decisions of programming the device. Now, I don't program the devices, but I work in conjunction with the EP practitioners who do.

So having some basic knowledge of that is very key. We need to refamiliarize ourself, potentially, with the pacemaker code. This is the North American Society of Pacing and Electrophysiology. We've been using this code for decades.

It has been updated a few times to involve five positions. A lot of times the pacemaker code is referred to just utilizing those first three positions to explain which chamber of the heart is paced,

to explain which chamber the pacemaker senses that electrical activity. Position III, shows what that device does in response to that electrical activity. We could spend a lot of time here too.

So through the eyes of a patient, there's many ways that you end up going to a cardiologist,

could be from a primary care referral, some family history, some of these diagnoses that are listed here, if you're a smoker, also some physicians and insurance companies are utilizing a heart risk profile.

If your considered heart age on the profile is higher than your actual age, and that involves your current lab values and recent vital signs and whatnot. Now, of course some of those patients, a group of those, will end up

needing an electrophysiology. These are the main reasons and indications for EP. To define the diagnosis of the arrhythmia, to establish the etiology for syncope, for those patients at risk for sudden cardiac death, and also to evaluate if a therapy

like ablation surgery or a device is needed. Can also be if a patient has declining heart function as well as decreasing ejection fraction. So, of course, cardiology can order a whole slew of diagnostic testing, chest x-ray, EKG, stress test,

we could talk for a long time about the whole cardiology pathway that sometimes does involve imaging, also can involve procedures and whatnot.

talk about some more non-invasive ways

to image the lymphatics there's non-contrast at Marlon Payne geography this has been around for a greater than a decade we basically do a tea to fats at sequence and we basically really amplify the signal difference between

fluid and soft tissue and we really want to focus on fluid that's very slow moving so this is very good for people of lymphedema cirrhosis venous malformations etc you're gonna get very nice images it's non-invasive gives you

good spatial resolution but you can't see small structures and you don't have an idea of how things are flowing so just to kind of show you an image from my training and right there where the arrow is showing you the thoracic duct

right next to the aorta obviously fairly distended what I did actually in this patient as we were doing research to generate these images actually giving them didn't mr gave him a milkshake put him back in the mo and you see this

little thing plump up and is actually really cute dynamic a Marlon pan geography is a newer technique that's come along where basically we've combined what we do with nodal and faint geography where we put a needle into the

lymph nodes with what we do with regular mr which is to inject gadolinium we dilute the gadolinium we can inject it right into the lymph nodes and now you can have flow dynamics as well as faster mapping of what's going on with the

lymphatics a very useful technique that I use in complicated leaks in pediatric patients etc

Hi. My name is Marshall Sussman. I'm an MRI Physicist at the University Health Network and the University of Toronto. This is going to be the second part of the k-Space and Gradients Lecture. In the first part we did a brief review of Fourier transform theory signal generation, and we used that to show how you can localize spatial position in 1 dimension using gradients. And we showed how the gradients are related to k-space, and in particular, how you can

use them to move you around in k-space. In this lecture, I'm going to extend that concept to show you how we can use gradients to localize spatial position in two, and in fact, higher dimensions. So just to review what we did in the first part of the lecture, we had the example of jugs of water at three different spatial locations. We turned on a linear magnetic field gradient that caused

a variation in magnetic field at the different spatial positions. As a result of the magnetic field gradient, each spatial position saw a slightly different frequency, sorry, a slightly different magnetic field, and the magnetization at each location rotated at a different frequency, and therefore, emitted signals at different frequencies. The overall signal that

we measured is a composite of all those signals. And that we applied a Fourier transform to generate the resulting distribution of signal. Now let's move to the more complicated situation where instead of just having a one dimensional distribution of water, we now have a two-dimensional distribution. So the amounts of water varies as a function of both the x spatial position and the y spatial position. So this is obviously a more

complicated situation because here we wanna be able to generate an image that looks like this, where we determine the relative distribution of water in two dimensions. So the question is how do we do that? To localize spatial position in the x dimension, or in this one dimension here we applied a magnetic field gradient along that x direction. So by extension, to localize spatial

position in the orthogonal direction we have to apply a gradient in the orthogonal direction. So this is a magnetic field gradient that varies as a function of the y spatial position. Now, if we look at this particular jug of water here, what you can see is that the magnetic field seen by this jug of water is dependent on both its x spatial position, so it has a magnetic field that's

determined by the x gradient, as well as the y spatial position. So depending on where it is in both x and y, each of these jugs of water will see a different magnetic field. So the overall magnetic field, as I said, has a component both due to the x and the y gradients. So that means that the signal frequency at every spatial position will depend both on the x and the y gradients,

and that complicates the interpretation of the data. So how do you analyze and reconstruct the image in this particular case? So instead of going through a detailed illustration of how that occurs, which is sort of beyond my level of PowerPoint capability, I'm just gonna go to to the mathematics of it because the concept very easily extends from the one dimensional case. So here I showed you, in

the case of one dimension, the signal as a function of time was given by this. In two dimensions, it's relatively easy to extend that. So now the distribution of water instead of just varying with the x position, now varies as a function of both x and y, and the frequency of the signal now

is dependent both a sinusoidal variation on the x position and the x gradient, times a sinusoidal variation of the y position and the y gradient. And the overall composite signal is gonna be a sum over all the

x and y positions. [BLANK_AUDIO] In one dimension, we applied this k substitution where we had the k variable is equal to gradient times time. And from that, we were able to produce this signal as a function of k-space position, which was just sum of, again, the distribution of water, which is what we're ultimately interested in, times the sine of this k variable times time. So two dimensions

is exactly the same manner, except in this case, we need two k variables. We need an x k variable, which is equal to the x gradient times time, and a y variable, which is equal to the y gradient times time. And the overall signal, as a function of kx and ky, will be given by this formula here. So in this case, you can see again, we have the distribution of water here and we have multiplied by the x k

position and the y k-space position. So now, with these kx and ky variables, we can now describe two dimensional k-space trajectories, as I referred to in the previous lectures. So let's just see how that works. So before you remember that we have signal evolving as a function of the both kx and ky variables. So if I just turn on an x gradient then we know that we're just moving

along the x direction of k-space. So I haven't done anything with the y gradient, so my y gradient is zero, so there's no movement in ky. So as I turn my x gradient on as a function time, I'm just simply evolving in k-space as a function of the kx position. On the other hand, if I put my y gradient on, if my x gradient is now zero, that means I'm gonna be moving purely in the y direction

of k-space. So if I put these two together, what you'll see is that I'm essentially moving in orthogonal directions in k-space. So here I have my full k-space data matrix, and the kx variable just simply corresponds to the horizontal direction, and the ky line corresponds to the vertical direction. So by turning on these X and Y gradients, I can move in orthogonal directions

of k-space. And as I've showed you earlier where we had 1D, you can produce many different k-space trajectory, but now we have more full flexibility of two dimensional variability. So let's say, initially, I turn on my x gradient as a function of time. So I just simply, in k-space, I'm evolving in the kx direction. If I then turn on my y gradient, that moves me in the in the horizontal, sorry, in the vertical direction, so I

move up a line of k-space. And if I didn't repeat that procedure by turning on the x gradient again, I then sweep out another line of k-space. Again, because I've already turned on my y gradient, I'm up at a higher ky position, so I'm not acquiring the same k-space data anymore. I'm acquiring it at a different

ky line. And I then repeat that procedure, but in this case, I acquire with a larger ky variable, so that's gonna move me up to a larger line in k-space. And I can then repeat that. So simply by repeating this sort of pattern, acquiring x, turning on my y gradient a little bit, acquiring another x, and turning my y gradient to a larger amount, I'm gonna be acquiring different lines of k-space each time. And

they're gonna be adjacent to each other rather than in the same one. Because each time I'm putting on a different y gradient, I'm going up to a different position along the y direction of k-space. Now, in general, as I mentioned before, there's an infinite number of possibilities, so you could also get to the equivalent position in y k-space by having a gradient that's half as large but twice

as long. And that's another alternative way of doing it. That's typically not the way it's done. Typically, you acquire a series of gradients that have the same duration but different amplitudes. So now with what we've described, we can now have a fairly complete description of a basic pulse sequence. So that's this diagram here which shows

you a basic gradient echo pulse sequence. And we all know what all the different elements of this pulse sequence do. So the RF pulse tips down the magnetization so we can get a signal, the Y gradient moves us to different lines in k-space, and the X Gradient is where we read out a line of k-space. Typically, the Y Gradient is depicted by this ladder-like appearance to indicate that on each iteration

of this, we increase the amplitude of the Y Gradient. Now one additional piece I have to mention is that, as I said, the data acquisition, when we turn these gradients on, that's always moving us to k-space. We're always going to different positions in k-space. But we don't necessarily have to take a measurement at every position in k-space, and as we'll see in later lectures, there's a good

reason why we don't do that because it makes the interpretation of the image more difficult. So there's often typically a fourth line in these pulse sequence timing diagrams that indicates when we turn our data acquisition on and off. In this case, you can see that the data acquisition is concurrent with the X Gradient, so that's essential when we're reading out each line of k-

space. And, as I had mentioned in previous lectures, the x-gradient is called the "readout gradient" typically, because that's when we read out the k-space data, and the y gradient is called the "phase encode" gradient. So just a bit of MR jargon that's used quite commonly. And as I've also mentioned in previous lectures, for any pulse sequence, the k-space trajectory can just be determined by looking at what's

going on with the gradient waveform. So, let's say you see, in some paper somewhere or some journal, these gradient waveforms that are used, so this is quite a complicated ones, and you have to say well what is the k-space trajectory that would be produced by this? Well you just simply have to run them through these formulas and determine what the effect is on the

k-space trajectory. So, for an example, in this k-space trajectory that you follow is this spiral trajectory, and for this particular sets of gradients the k-space trajectory you follow is this one here. Now one thing I should just briefly mention, just for the purists who might be watching, this description of the k-space variables, gradient times time, is a slight simplification

from what really occurs. So it's actually slightly more complicated than this, but I'm not gonna go into that for these lectures, just in the interest of simplicity. Now, up to now we've been talking about two-dimensional imaging. But you can also extend this exact same concept into imaging in three dimensions. So as before, you can imagine here we have the signal as function of time in two dimension with

two orthogonal gradients x and y. So, obviously, you could probably guess that to make this a three-dimensional image we could just add in a gradient in third orthogonal direction. So a gradient in the z direction as well. And the signal is just a simple extension of the two decay. So now we have, again, a distribution of magnetization that depends on x, y, and z, and now we have three

terms that determine the frequency of the signal Gx, Gy, and Gz. And to define our k-space variables, in the case of 2D, we had two variables kx and ky, and three dimensions we have correspondingly three variables kx, ky, kz. So again, the interpretation the signal is exactly the same. The signal evolves as a function of all three of these k-space variables. So it's slightly more complicated,

but the basic content is exactly the same. So now instead of having a 2D k-space trajectory, we would have a three-dimensional k-space trajectory, so we'd have to essentially fill up a cube rather than just a square. [BLANK_AUDIO] So a 3D pulse sequence looks like what you see here. So in this case, now you can see we have x, y, and z gradients.

And again, the y and z gradients have this ladder-like appearance because, again, we're increasing the y and z gradients on each subsequent iteration of our acquisition scheme. And again, we typically have one read out direction, along the x, and the y and z, in this case, two phase encode directions. Now one thing I should also mention at this stage is that, previously, I was talking about

a three-dimensional scan. You can also get effectively three-dimensional information by doing what's called a 2D multislice scan. So this is essentially where you acquire a series of two-dimensional images that correspond to a very thin section of the anatomy. So previously, when we did a full three dimensional scan, we had this pulse sequence here. We had phase encoding in the y and z gradient. In the case

of the two-dimensional scan, so we only have to have gradients in the two dimension cause we're only encoding a two-dimensional image, but what we do is we just excite only a thin section of the anatomy. And we do that by turning/g on our z gradient with the RF pulse, and that's called

a slice selection. And then we acquire data acquisition just as before. So those are the two different methods of acquiring three dimensional scans. You either acquire a full 3D scan where you're phase encoding in two dimensions, or you can acquire a 2D multislice scan where you're only imaging a thin slice of the anatomy at any one time, and you're encoding the two dimensional image at different positions

along the anatomy. So just to summarize what we've been through in this talk. So we've shown that gradients create a mapping between spatial position and frequency. We showed that by using a Fourier transform we can determine the relative signal content at different spatial positions. We've shown that the resolution is inversely proportional

to how far we were in k-space. Shown the field of view is inversely related to the spacing of the k-space. And we've shown that gradient in orthogonal directions can be used to encode multidimensional images. So if we need to encode two dimensions, we need two orthogonal gradients, three dimensions we need three orthogonal gradients. And we've shown that the k-space trajectory is determined

directly by what's going on with the gradients. The x, y, and z gradient correspond to an x, y, and z position in k-space, that's it. [BLANK_AUDIO].

to our case study the first case study is the normal whole body pet MRI the the

image song to your left it's a regular pet MRI the one on the right as you could see it's a big difference there is very vivid image and you could pinpoint the organs they are not to me of the patient this is normal

scan there is normal uptake on the brain the ureters the bladder the kidneys those are normal there's no abnormal uptake or there's no hypermetabolic uptake noted the next case study is a 59

Hey my name is Marshall Sussman, I'm an MRI physicist at the University Health Network and University of Toronto. I've been giving a series of lectures on basic MR physics. And this lecturer is gonna be the second part of advanced concepts in MRI. In the first part of this lecture we went through the concepts of spectroscopy, MR safety and SSFP. In this part of the lecture I'm gonna focus on parallel imaging, diffusion and propeller. So let's start out

with parallel imaging. So what is it? It's just simply a method for reducing the amount of data we need to acquire in order to generate an image. And there's a number of different techniques related to parallel imaging that have different names depending on the vendor and depending on the specific implementations, so some examples smash, sense, asset and the list can go on. So why

is it that you wanna reduce the amount of data that you need to acquire?. Well the reason is because it reduces the overall scan time. So just to give you an example of that, let's say we start up with a convectional acquisition, where we're gonna require a complete set of k-space data. Now, what if we require only every second line of k-space rather than the full

accent of k-space. So we reduce the amount of data we need to acquire, and what's the effect of that? Well we know that in convectional imaging if we simply did that, the field of view is inversely proportional to the space scene of our case base samples. So if we increase the space in between our k-space samples, that means our field of view's going to be reduced. And in general, so if for example

if we dropped every other line of case base, we'll drop our field of view down by half. And we know from the artifacts lecture that in general that's going to lead to aliasing, the field of view gets to be smaller than the anatomy or imaging. So just as an example of that, here we have an image of the brain, and if we had a very small field of view, then we would get aliasing. So obviously

that's gonna be unacceptable if the anatomy we're interested in, lies in the alias region. So with parallel imaging this is essentially a mathematical method that allows us to remove the aliasing. So we could get an image that essentially, completely removes the imaging using parallel imaging. So again, what's the advantage? So it's to reduce scan time. So let's just say, for

example, that it takes five milliseconds to acquire a line of k-space. So the overall scan time is gonna be equal to, five milliseconds times the number of lines we have to acquire. So if we have only half as many lines we need to acquire, then we're gonna reduce our overall scan time by half. So you can see this week, it allows for a significant take, savings in scan time. Now depending on

the parallel imaging technique you use, it's possible to eliminate more than one line of k-space. And in general, the number of lines you eliminate. is called the acceleration factor. So if I eliminate, say every other line of k-space, I have an acceleration factor of two, if I eliminate three lines of k-space, then I'm going to have an acceleration factor of three.

what's the limit? In general the acceleration factor has to be less the number of coil elements. So if I have for example 16 coil elements in my multi channel coil, and I can only image up to a maximum acceleration factor of 15. In practice the limit is often significantly less than that so this is sort of a maximum theoretical limit. So what are the disadvantages of parallel imaging? Well, first

of all signal to noise ratio. So we know that signal to noise ratio is proportional of the square root of scan time. So if we reduce the scan time, then that reduces the SNR. So that essentially a penalty that's inherent to any imaging technique which images faster. So the shorter the scan time, the lower the SNR. However, in the case of parallel imaging, there's an additional parallel imaging specific

penalty. And this is called the G-factor penalty. So this is an additional SNR penalty on top of the one that we take that's associated with reduction of scan time. So just to give an example of that, here I have three images of the brain, acquired with an increase in acceleration factor so two, three and four. So what you can appreciate is that, as we increase

the acceleration factor, in other words as we reduce the scan time, the signal to noise ratio, decreases. So that's expected because again we reduce our scan time, lessen our proportional scan time, so we reduce our scan time or SNR decreases. However, scan time penalty, should be uniform across the image so we should expect a uniform SNR penalty. But you can appreciate most prominently

in the acceleration factor equal to four, that the signal to noise ratio actually varies non uniformly across the image. And that non uniform loss, is the additional loss associated with this G-factor penalty. So you can appreciate that we have again specific penalty of parallel imaging with SNR as well as the more general penalty of the reduced scan time. Well also, another disadvantage of parallel

imaging is that in some cases there can be some artifacts that are specifically associated with parallel imaging. If the reconstruction doesn't end properly or if the setup isn't correct, then this can lead to some specific artifacts associated with parallel imaging. Now as I mentioned there's a number of different flavors of parallel imaging,

two of the sort of the earliest ones and the most widely cited one are smash and sense and these just are variations on the way that the data is dealt with, so smash operates on case based data while sense operates on the image data and there's many different versions of this, some that hybridize various different components of this, and many different flavors of parallel imaging. Next we're

gonna move on and talk about diffusion weighted imaging. So diffusion imaging is a technique in MRI, that generates contrast between tissues based on the microscopic motion of water. In other words based on differences in the microscopic motion of water. So in general water that can move freely around or is very mobile, tends to have a different signal and diffusion images than water

that's so called restricted or has much more difficulty in moving around. So for example, syrup which is much thicker will have a different signal than water would on a diffusion weighted image. So what is the pulse frequency used for diffusion weighted imaging? So the basic pulse sequence is shown here and this is somewhat of a simplification that illustrates the basic concepts. So as we've

shown many times in the past, we have our initial RF pulse that tips on magnetization and you can see we have our x and y gradients in our data acquisition, which actually includes the image, so this is essentially the imaging part which you've seen many times before. But in diffusion wave imaging, there's an additional component added prior to imaging and this is the part that actually encodes the

diffusion encoding. And there's different ways of doing this but the one I've shown you here, is a pair of opposite polarity gradients or positve and negative gradient that we apply before we then capture the information in our imaging components of the pulse sequence. So what do those offset polarity gradients do? Why do they encode motion? So let's consider the case where we have a molecule that

initially lies at the position that you see here. And let's also assume that we've turned on a gradient like you see in the image above. So we know from our basic MR physics, that because the molecule sees a certain magnetic field it's gonna rotate at a particular frequency, so over time the [INAUDIBLE]

associate with the water molecule will rotate. So that's the positive polarity gradient. If we then turn on the negative polarity gradient, so the magnetic field that it sees, is now much smaller so it's gonna then rotate in the opposite direction. So if we turn on that gradient for the exact same amount of time as we turn on the positive polar ingredient and after a period of time, magnetization will

go back to its original position. So in this case the net effect of this gradient, of these two opposite polarity gradients, will be essentially nothing to the phase of that molecule. So now let's consider a second situation, where we turn on this positive polarity gradient, but in this case, let's consider what happens when the molecule is moving. So initially,

let's say we turn on the gradient, and the magnetization associated with that molecule, again accumulates phase. Let's now assume that this water molecule, moves to a different position. Okay, we then turn on our gradient of the opposite polarity. So again, now, the molecule sees a different magnetic field strength. However, it's not exactly the same one, as we saw in the first case. It's

not the opposite of the one we saw in the first case. So it will rotate in the opposite direction, but we'll do so at a different frequency. So after a period of time, after you've played out this opposite polarity gradient, it won't re-phase back to exactly the same position, because again, it's experienced one gradient of one amplitude on the positive

polarity, and it's experiencing a different gradient when we have a different magnetic field on the negative polarity. Now, if that instead molecule instead will move to that position had moved to a third position, then again the magnetic filed that it will see, will be again different. So, depending on where that molecule moves to, that will determine what sort of magnetic field it will see.

And that will determine how much it's actually re-phased. So if it sees that it hasn't moved and it see exactly the same magnetic filed then it will be perfectly re-phased. If it sees a larger difference in magnetic field, in other words if it moves more, it will have a of less complete re-phasing, so in general, after the diffusion encoding part of our pulse sequence the water molecules will have

a phase that will depend on the amount of diffusion so if things diffuse faster, we'll have a different phase with things that diffuse slower, or things that are stationery and it's that difference in phase which is what we're encoding in the diffusion coding component of the pulse sequence. So up to now we've been talking about diffusion effects on an individual

water molecule, but now let's step back a little bit and look what happens over a region of tissue when we have a large number of water molecules. So in general, here's one water molecule, there's another one, and in general we'll have many water molecules distributed throughout the tissue. Now, due to diffusion, the water molecule will de-phase relative to each other because each water molecule

will experience a slightly different motion, because of course it's a random Brownian type of motion. But depending on how quickly these water molecules are moving around, will depend on the spread of different motions that are seen and this will in turn affect the amount of dephasing. So the amount of dephasing that we have will then in turn directly be proportional to the signal loss.

So for example, if we have water molecules that are completely immobile, then they'll all have the same phase as a result of that of that gradient, those two gradients of opposite polarity. If molecules are moving on very very rapidly relative to each other, then there'll be a large number of different phases after we play out those two opposite polar ingredients, so have a greater distribution of phase

and therefore a greater signal loss. In general again the more freely water can move around, the greater the distribution of phases after we play out the two opposite polar gradients and the greater the signal loss. So the more diffusion, the greater is the signal loss on following diffusion in coating. So up till now, I've shown you how in tissues where water can move around more freely, we'll

tend to get a larger signal loss upon diffusion encoding however there's also additional factors will lead to the pulse sequence itself it can also affect the signal loss. So in particular, properties related to the diffusion encoding ingredients, will have a strong effect on the amount of signal loss we have, in particular the strength, length

and separation between our two diffusion cord ingredients, will the amount of signal loss is related to something called the B-value. So this is just a parameter that takes into account those three different factors I just mentioned. So I won't go into the mathematical derivation of it, I'm just gonna present the result for you as

you see below. Showing you that the B-value is the factor that determines the amount of signal loss based on the pulse sequence parameters. Mathematically, again, I won't go through the derivation I'll just present the result, we saw that the signal loss, varies exponentially as a function of the B-value. So the same loss we get is E minus B, which is the B value times something called the

apparent diffusion coefficient. So the apparent diffusion coefficient is related to have freely water can move around. So this is very similar to what we've encountered before with T2 decay. So T2 decay call was also an extension signal loss but In that case we had T, the parameters T and T2 rather than the B and the diffusion coefficient. So in this case, the B value correspond

to time so in expense to T2 decay it says time went on, the amount of exponential decay increased, in this case as the B values increase, we get a larger amount of signal decay. In this case, the apparent diffusion coefficient, corresponds to one over the T2 value so again apparent diffusion coefficient just relates to how freely water can move around, the more freely water can move around, the larger

the apparent diffusion coefficient. So again as I mentioned earlier, things with the larger apparent diffusion coefficient means water can move more freely and therefore that leads to a larger signal decay. Now, just like in T2 imaging, where we get T2 wave of contrast we can also get a diffusion wave of contrast. So in particular let's say we have a tissue that has a different apparent diffusion coefficient,

then if we image with a particular B value, we can generate a diffusion wave of contrast. So this is just the same as we generated T2 way of contrast by choosing a particular echo time, so to generate a diffusion way to contrast we choose a particular B value, that correspondingly generates a contrast based on differences in the apparent diffusion

coefficient. So in general, the larger the B value we use, the heavier the diffusion wave of contrast in other words the more of a difference in signal we can get between tissues with different apparent diffusion coefficients. So this is similar to the TE parameter and T2 way to the machines so in that case the larger the TE value, the heavier the T2 wave of contrast. So here, we have an example that illustrated

here where we use a larger B value we get a larger difference in signal between the two tissues with different diffusion coefficients. However, one thing that should be pointed out, is the larger the B value, in general the lower the overall SNR so we can see that the contrast between two different Issues increases but the overall SNR of both of the tissues decreases.

So in general if we use too large of a B value, while we may get a large contrast between the tissues, the overall signal will be too low to actually use it to generate a meaningful image. So diffusion wave imaging has many different applications. One of the most successful is as an indicator of acute stroke. So in the brains of people who've had an acute stroke. Because pain on the diffusion would lead to

coefficient that is a very strong detector of the presence of particular types of strokes and I'll show you an example that you see in the image that I'm presenting here. [BLANK_AUDIO] Now, one thing that you can appreciate from the description I had given you up to this point, is that we turned on gradients in the

y direction, and what that allowed us to do, is encode motion that occurs in the y direction of the same direction as the gradient. However, we can also encode diffusion information other directions as well. So for example, if we turned on a series of opposite clarity gradients in the x direction, and that would encode diffusion information in the x

direction. And in general, you can imagine that these may not be the same. so for example let's say we have a nerve fiber, you can imagine that the water can move more freely along the direction of the neural fiber rather than in the perpendicular direction or the cross sectional direction. So you can imagine that water, the apparent diffusion coefficient, would be different in the two different

directions of fibers. We can actually extend this concept to essentially map out the direction of diffusion and we can generate what I call fiber tracts or tractography images of the brain. So here's a nice example of that, it shows you images of the fiber tracts in the brain, based on again the direction of water diffusion. You can also see the same sort of thing in other tissues, here we have

an example of tracking fibers in the calf muscle. So this now is supposed to tracking neural fibers as in the previous case, this is now tracking muscle fibers. And again, using the same sort of diffusion encoded type of information. Last topic we're gonna discus today is related to propeller. So I'm gonna walk through the basic pulse sequence underlying propeller.

So let's say of here we have our case based data set, so in propeller, just as in all the previous case-based acquisition strategies we've gone through in detail before, require a series of lines of case phase just like you see here. So if this was a normal gradient echo type pulse sequence, we would just continue this until we acquire the full extent of k-space. however, the key concept in color

is that we only acquire limited set of k-space date, or case phase lines. We than next we rotate those lines, and reacquire them. So we rotate which lines of k-space we're then gonna be acquiring. And we continue doing that process one after another, and you could see why this is called propeller, because the regions of case phase require and essentially

rotate like the blades of a propeller. Once we do that, for a specified number of times, we can then fill up the entire extent of case phase that we need to generate. So you can see that from the middle image here. Now, notice with this sort of approach, we're essentially over sampling regions in k-space. So for example the region at the center, we basically acquire on every single blade of the

propeller, where as the outer regions when we only acquire on one or a few blades of the propeller. After we acquire the full extent of k-space as per usual we acquire apply for a transform and this generates the resulting image that you see on the far right. [SOUND] Now, as I mentioned, with this approach, we're essentially over-sampling the center of k-space. On every single acquisition, every single

bleed of this propeller, we're repeatedly acquiring the center of k-space. Now what happens if we reconstruct an image just from the center of k-space? Well, recall from our earlier k-space talk, if we reconstruct images from the center of k-space that will give us essentially a low resolution image of the anatomy like you see on the image

on the right table. So on every single blade of the propeller, we can form a low resolution image of the anatomy. Now that's actually used to correct for motion. Because, let's say motion is occurring on subsequent acquisitions of each of the propellers like you see in the image that I've shown you here. So on the first image we have the brain in the central position,

we move to acquire the second however, you can see the brain has moved up, and a third blade propeller, the brain has moved down to a lower right-hand corner. So whenever you blade the propeller, the anatomy itself has being moving around. Now if we were to just simply reconstruct the image at this point without taking any further action, obviously we would have motion

artifacts, because the data would be inconsistent from the different blades of the propeller. However, the key in the propeller is that since we can form a low resolution image on each of the blades, we can detect the motion and ultimately correct for it. So we can correct individually each blade of the propeller before we combine the data together, and we can then eliminate or minimize the amount

of artifacts we have in our resulting image. So once you apply a transform to the corrected image, we were gonna get an image that's free of artifacts. So just to summarize, so with the power we can correct for both translations and rotations in the image. So that's the major advantage underlying propeller, so it corrects for motion. There are a couple of disadvantages

however. So first of all, in general propeller scans take longer than a corresponding rectilinear case-based acquisition equivalent. And the reason is again cuz we're oversampling portions of case fails, so for oversampling in general that's gonna take longer. 50% increase in scan time is not unheard of using a propeller

approach. The other disadvantage of propeller is there's no defined phase and frequency in code direction. So because we're rotating the blade of the propeller we can't say, with the horizontal direction is phasing code, vertical direction is frequency in code, because in general phasing frequency code direction will change on every acquisition. So this can lead to a number of drawbacks with respect

to floor artifacts and also aliasing artifacts. So for example in case of an aliasing, there's no one dimension that will be free of aliasing artifacts. If you recall one of the tricks we played with our rectilinear acquisitions, we like to place the long dimension of the anatomy in the frequency encode direction because aliasing doesn't occur in the frequency-encode direction. We can't use that

trick with propeller, because there is no single frequency encode direction. So in general aliasing will occur at all the positions of the anatomy if our field of view is smaller than the anatomy. Here, we just have an example of propellers. So the image on the left, was just acquired with just a conventional fast spin echo pulse sequence, so this is just a convectional rectilinear

acquisition and the image on the right was acquired with propeller. So both of these images were acquired when the patient was in motion. And you can see in a conventional fast spin echo image we have severe motion artifacts, whereas in the case of propeller We've essentially eliminated virtually all of these artifacts. So this is really the main advantage

of propeller and this is one of the major reasons why it's coming quite utilized in patients scans. So that brings us to the end of the second part of the advanced concepts in MRI lecture, thank you for listening and I hope you'll join me

for the subsequent lectures. Thank you. [BLANK_AUDIO]

Okay, you're gonna see this top headline quite a lot, the collaboration of cardiology and radiology. This is fundamental. Now, I'm gonna give four basic steps to this process, first one being is to gather the stakeholders and find a way to work together.

I gave a list of who's all involved in this process at our institution in both radiology and cardiology. The people who have the buy-in and make the decisions are the ones who definitely have to be in the room

deciding to work together on this, but all of these people will be involved in the process.

The Heart Rhythm Society also gives some indications about the non MRI-conditional or off-label devices. And basically, they are saying the Class IIa recommendation would mean

there's a moderate strength of recommendation the benefits still outweighs the risk. So they are saying, as well, that it's reasonable to perform these scans with off-label devices, as well. Again, there's some conditions involved

with that including no fractured, epicardial or abandoned leads. Definitely if the benefit outweighs the risk as far as physicians are concerned it's clinically warranted, personnel is available, also that,

I can use my pointer here let me try. This one right here, the follow up within one week if there are these specific parameter changes. So that's really information for the electrophysiologist,

but it is intriguing to me that even now these major societies are saying it's reasonable to perform these scans even in the off-label device patients.

This is a little bit of our timeline as it relates to the conditional pacemakers and ICDs. The first conditional pacemaker was made in 2008. And in 2011, it had FDA approval in the U.S.

So we began scanning these patients with these devices in 2014 on a pretty limited basis. In 2015, the radiology nursing staff took over monitoring of these patients, and at the present time we have

electrophysiology mid-level practitioners who monitor patients who have the off-label devices. Here just a few pictures of an MRI-conditional pacemaker, these are just quite small, can be even like four inches by two inches.

This is a Medtronic one. Here is a ICD made by Boston Scientific.

Hi, my name is Marshall Sussman. I'm an MRI physicist at University Health Network, in University of Toronto. This lecture is gonna be the second part of my Relaxation and Image Contrast in MRI talk. So in first part of this lecture we briefly reviewed what image contrast is, and then we discussed a number of different MR mechanisms that can be used to generate contrast. So in this lecture what I'm gonna do is, I'm going to show how those contrast

mechanisms can be used within various different MR pulse sequences to create contrast. So the pulse sequences that I'm gonna be talking about are gradient echo and spin echo. So image contrast in image depends on a number of factors. So first is the tissue parameters. So this is proton density, T2, T2*, T1, which we've obviously mentioned, pulse sequence, and the pulse sequence parameters. So all three

of these things will affect the ultimate contrast we see in an image. Now often, when I interact with people who aren't necessarily really familiar with some of the details in MR physics, often, various different mnemonic devices are used. So people say if I have a short tear in my pulse sequence that gives me a T1-weighted contrast image. If I have a long TE

that gives me a T2-weighted contrast in my image. And that's true for many pulse sequence types, but it's not true in all cases. There are certain pulse sequences where these sets of parameters could give you different types of contrast. And really, in order to fully understand the contrast you're gonna generate in an image you really have to examine the pulse sequence, the parameters, and the underlying

contrast mechanisms. And it's really only from that complete understanding that you can really get a good comprehension of what types of contrast you're going to be generating. So with that in mind, let's move on to the first pulse sequence that we're gonna look at, Which is gradient echo. So this is one that we've looked at, a number of different times in the previous lectures, but I'm just gonna

briefly review it here. So basic pulse sequence timing diagram, you can see here, it starts with an RF pulse, which we know tips down the magnetization away from the external magnetic field. And we then turn on our gradients which is what move us through k-space to acquire our data. So initially, after we do our RF pulse, we always start at the center of k-space. We then turn on our Y gradient, and that

moves us up into a particular position in the vertical direction. We turn on our negative polarity X gradient which moves us out to the left hand side of k-space, and then that's followed by our positive polarity gradient, which sweeps out a complete line of k-space. And that's when we acquire our data. We then increase the strength of the Y gradient, and that moves us up to another

line in k-space, and repeat that same procedure. So we just simply step through this, repeat this procedure as many times as necessary in order to cover off all lines of k-space. Now within this basic gradient echo pulse sequence, there's three different adjustable parameters that we can manipulate. The first is the Flip angle or theta so that's really the RF pulse, and it's just simple

the angle that the magnetization makes with the external magnetic field. So this is something that we can control. The second parameter is the TR or the repetition time. And this is simply the time between the acquisition of adjacent k-space line. So essentially the time that it takes us to repeat this cycle. Third is the TE or the echo time, and this is defined at the time between when we first tip

our magnetization down, so we first put out our RF pulse and the time with which when we acquire the central portion of k-space. So in this case it's the center point of this gradient loop, cause that corresponds to when we go through the center of k-space. So in the next few slides I'm gonna show how these parameters can be used to modulate the contrast we see in an MR image. So first of

all, is the echo time or TE. So as we saw from the first part of the lecture, after we play at our RF pulse, the signal intensity decays away over time with T2* decay. So here we have, let's say this tissue here has a T2* value of T2A, T2A*. So the signal decays away like you see here. [BLANK_AUDIO] Now at this point in time here, when we go through the center of k-space which corresponds to this echo

time, the signal has therefore decayed away up till this point here. If we now have a second tissue with a different T2* value, so indicated by the red line, T2B*, then at the echo time, which again is the time we go through the center of k-space here, you can see that the signal will have decayed away, a different amount than the first one, again because it has a T2* value.

Now the contrast we're gonna get in our image, is simply gonna be the difference in signal intensity, at this point, at this echo time. So it's just gonna be e to the -t over T2A*, minus e to the -TE, over T2B*. So by setting up this echo time, we generate a T2*-weighted contrast in our image. Now what if we move the echo time to an earlier time point? So we've

now shortened the echo time TE to this point here. So again, the contrast we're gonna get in our image, is again just simply the difference in signal intensity between these two lines here. But in this case, because our echo time is shorter you can see that we haven't generated as much contrast, so there's less contrast in this case. So in general, when we shorten the TE we reduce the

T2*-weighted contrast in the case of gradient echo pulse sequence. Now I've been mentioning here that, the echo time, the TE in code contrast, which corresponds to the time we go through the center of k-space. But why is it the center of k-space is important, what's special about it? Well here we have a slide that I showed in my earlier talks on k-space. Here we have the

complete image and the corresponding k-space data that generates that image, again recall via Fourier transform. Now if instead of generating the image from the complete k-space data set, I just generate an image from the central portion of k-space, resulting image you see looks like this here. So in this case you can see, importantly, that we encode the contrast in this image.

So the center of k-space still generates the contrast that we see in this image. The outer portion, so if we reconstruct the image just from the outer portion of k-space, you can see that we see the edge information, but there's really no contrast in the image. The image is essentially a uniform color. So that's the reason why the center of k-space is important, because that really encodes

the contrast in our image. So that's why we care about when we go through the center of k-space, and that's what we set our echo time to be. So just to summarize what we've got to date. The echo time, TE, corresponds to the time of acquisition of the center of k-space. The center of k-space, as we know, determines the image contrast, so therefore as a rule of thumb, the longer the TE, the heavier

the T2* weighting. So the more difference in T2* weighting we're gonna have between tissues with different T2* values. So here I just have an example of, this is an image of the heart, one acquired with an echo time of 2 ms, and one acquired with an echo time of 16 ms on a gradient echo scan. So if we compare say the myocardium and the blood in these two cases, you can

see in the one with a 16 millisecond echo time, there's a much larger contrast between the myocardium and the blood. Again because the myocardium has a much shorter T2* value. So it decayed away, much ore dramatically, in the 16 millisecond case, than it had in the 2 millisecond case. The next pulse sequence parameter we're gonna talk about is the

repetition time or TR. So recall the TR it's just simply the time between we acquired adjacent k-space line, so basically the time that we repeat this cycle. So let's look at what's going on with the magnetization. So initially we started out with our magnetization at its full, complete value. So let's say, as a value, for purposes of this discussion as one. So we then play out our pulse, and in this case let's assume

we have a 90 degree pulse, so that tips down the magnetization completely into this transverse plane here. Following that we have T2* decay. So the signal is then going to decay away completely. So following the magnetization decay, the magnetization will then regrow back towards its initial value due to T1 recovery. So here we have again this formula here, 1 - e to the -TR

over T1. Now if we set our TR to be long enough, then that magnetization will regrow back to its initial value. So in other words, the magnetization will regrow back to its initial state. But what happens if I set my TR to a shorter period of time, the magnetization doesn't regrow back to its initial value. Then instead of recovering completely, it will only recover partially. So let's say in this case, it only

recovers to a value of 0.8. So when we then foot the magnetization down, it now, instead of having a value of 1, it only has a value of 0.8. So when we then have our T2* decay on the next iteration, the signal intensity starts from a value of 0.8, and then decays away completely. It then regrows back to a value, again, of 0.8. It's decayed away completely, and we have the same TR value, so it goes back

to a value of 0.8. Now this is called, essentially, the steady state of the magnetization because the magnetization lies in a steady state on every single iteration. So it lies in exactly the same position on each iteration we do. So we tip the magnetization down, it decays, it then regrows back to a value of 0.8. We tip it down again, it decays away completely,

regrows back to a value of 0.8. So a steady state just simply means the magnetization is the same from one TR to the next. And in general, we almost always we'd acquire images when the magnetization is in a steady state. So now, let's examine the contrast it's generated as a result of a particular choice of repetition TR. So as we mentioned in the previous slide, this magnetization in

the red lies in the steady state. So we're constantly going between these two situations. We tip the magnetization down, it has a value of 0.8, it decays away to 0, then recovers to a value of 0.8, and the process is repeated. So it's the same each time. Now, this value of 0.8, the reason it recovered to this point, obviously, depends on the TR value. So if we change

the TR value to some other value, then the magnetization will recover to a different point. But the value that it recovers to also depends on the T1 value of the tissue. So for example, if I had tissue with a different T1 value as indicated by this green line here, so let's say I had a longer T1 value, then it would recover at a time TR to a different point. So in this case let's say it recovers only to

a value of 0.5. And important thing to note is that this magnetization also lies in its own steady state. So it's gonna continuously oscillate between, it's gonna decay away to, it's gonna be tipped down, and it's gonna decay away, and recover back to a value of 0.5. It's gonna be tipped down, decay away to 0, recover to a value of 0.5. If we look at this plot down on the right here, you can see that the red magnetization

is in a steady state where it has a value of 0.8, the green magnetization has a steady state value of 0.5. So in other words, we generate a contrast between these two signals, a T1-weighted contrast, which we capture when we tip the magnetization down. So in this case, the red magnetization would have a stronger signal intensity than the green one would.

And this is a contrast that depends on a T1 value. Now, if I change my TR, that's gonna change the relative amount of recovery between these two magnetization. So the relative T1-weighted contrast is gonna change if I adjust my TR. Now, as a general rule of thumb, the shorter the TR, the greater the T1 weighting because that means that the shorter one's gonna recover more quickly than tissues

with a longer T1 value. So TR affects the T1 weighting. Generally speaking, the shorter the TR value, the greater the T1 weighting. Now, there are some exceptions to this, of course, that if you have a very, very long TRs then the contrast basically disappears. And similarly if we had very, very short TRs then there really isn't much time

for a contrast to evolve so there overall won't be very much difference in signal intensity. [BLANK_AUDIO] Couple of additional comments on this contrast generated by TR. First of all, in this case, we always assume that the magnetization decays way to 0 each time. So that was sort of our steady state condition.

We tip the magnetization down, decays away to zero, then recovers back to it's steady state value. Typically, if that's not the case, we often tip away/g with something called spoiling. So that's just simply a gradient which dephases the demagnetization to get rid of any of the residual magnetization that's lying in the transverse plane. There's also RF spoiling, which is another way of

accomplishing the same thing. Another thing to consider is that I've assumed that when we tip the magnetization down we're capturing that T1-weighted contrast exactly as it's stored along the z axis here. But as we know, that if we have a TE that's not equal to zero, we're gonna start to have some T2*-weighted contrast evolve in our image. So if our TE is anything other than

zero then we're going to have, not only this T1-weighted contrast, but we're gonna have an additional T2*-weighted contrast that's superimposed on top of it. So we're gonna have sort of a mixture of both T1 and T2* weighting, which would complicate the interpretation of the image, cause it's hard to distinguish which mechanism is dominating. So typically, we wanna choose TE to be as short as possible in these

sort of gradient echo scans where we're trying to highlight T1-weighted contrast. We wanna choose TE as close to zero as we possibly can. Now let's move on to talk about the effects of flip angle on the contrast we have in a gradient echo pulse sequence. And again to examine this one we have to look at, again the magnetization, so in the previous case

we assumed that when we play our RF pulse we're tipping it down with a 90 degree pulse. So, in this case, let's generalize it, to say, we're gonna have an RF pulse with an angle theta. So, in this case, instead of the magnetization being completely tipped down in the transverse plane, it's only tipped down with angle theta. So at that point, instead of the magnetization, initially

had a value of one, but now in the transverse plane, where again we're our signal, it only has a value of 0.7 because we haven't tipped it completely down. So part of this magnetization lies along the z axis and part lies along the transverse plane. Okay, so once we do that, the signal we know decays away with T2* decay, just as before. So we assume that this signal disappears along

the transverse plane, and then once a signal's disappeared we're gonna then experience T1 recovery, and the magnetization is gonna regrow back to its initial value. But remember that unlike the previous case, it's not starting from zero because we didn't tip the magnetization completely down. Rather it's starting from this point of, in this case,

0.7. So the magnetization will then recover with this equation here. It's gonna start at 0.7 and recover back to its initial value. In this case, let's just assume we choose a particular TR, let's say, it's gonna recover back to a value of 0.9. And just to show you that this magnetization essentially recovers, this is basically the latter/g part of the T1 recovery curve. Cause initially, if we started

from zero it would recover like this, but in this case we don't have this portion of the curve cause we're starting at 0.7 already. So that's on the first iteration. We then have to tip the magnetization down again, but it actually gets to be quite complicated, because in general, the magnetization is gonna now start from a value of 0.9, it's gonna be tipped down to some value other than 0.7. And in general,

it's gonna take several iterations until we get into the steady state. So I'm not gonna draw the diagram here cause I 'd probably would have to go through four or five iterations of this until we finally get into the steady state. So generally speaking it's gonna take, if the angle is something other than 90, it's gonna take a longer period of time to get into this steady state of magnetization, but

we typically have to wait until we're in the steady state before we start imaging. Now what happens in terms of the contrast? So let's say we, again, flip our magnetization down with angle theta, it decays away, and then begins to recover. Now if we have two different magnetizations with different T1 values then obviously it will recover to different amounts. So if we plot the recovery on the

plot on the left, the shorter T1 value recovers more rapidly whereas the longer T1 value recovers more slowly. And again recall, they're starting from different points in time here at different values here. This is starting from value 0.5, this is starting from a value of 0.8. So in general, when we have different flip angles this also produces different T1-weighted contrasts. But unfortunately, the relationship

is quite complex between the two, and there really isn't a straightforward relationship between flip angle and T1-weighted contrast. As a rough guide, and this is only a very rough guide, larger flip angles generally produce a heavier T1 weighting, but that's not always the case. Here we just have an example, we have four different images of the brain acquired at different flip angles, 5 degrees,

20 degrees, 40 degrees, 90 degrees. And you can appreciate that the contrast of the brain tissue changes with differences in our flip angle. Okay, so let's just summarize what we've talked about so far with gradient echo. So we showed that there's three different parameters that we can vary in the pulse sequence. There's the flip angle theta, there's the echo time or TE, the time from the RF pulse

to the center of the acquisition of k-space, and there's the TR or the repetition time, which is just the time between the acquisition of adjacent k-space lines. We showed that if we have a longer TE, this is going to produce a heavier T2* weighting. If we have a shorter TR that's gonna produce a heavier T1 weighting. And finally, if we have a larger flip angle,

that's gonna, generally, produce/g a heavier T1 weighting although the relationship is complex, and it's not quite as straightforward. So now we're gonna move on to discussing some of the contrast mechanisms underlying spin echo. So just a brief review of the spin echo pulse sequence.

And to just kind of put it into some specifics, the main objectives I'd like to hit on is just summarizing the research that has been the basis for our protocol.

To explain some of the steps of those, the steps of the protocol. I'll speak a little bit about the off-label device protocol to give some nursing considerations and also a few moments to consider application to another setting.

We're gonna do that first by looking through the lens of a patient, and then through the eyes of a nurse. And then, taking a look at the greater process for an organization, and do a little synthesis at the end.

This gets a little busy, but this is our protocol for scanning patients with MRI-conditional devices. This does come straight out of that 2011 Nazarian and all,

that big landmark study that they did. Basically, there are conditions involving newer implants is what is preferred. The leads need to be implanted more than six weeks prior to the MRI. We talked about leads do not need

to be abandoned or not fixated. We do talk a lot about if the patient has an ICD that they are confirmed not pacemaker dependent. If they are that can be reason to cancel the exam. And then number eight and number nine,

talks a lot about programming. And then, 10, 11, is also what needs to be done, our electrophysiology, NPs, and PAs, are doing this of finding out these different readings of the device before and after the scan.

Number 12, monitoring at least of EKG, BP, and pulse oximetry. And then, that programming device remaining on. And then, having a follow up appointment for that device evaluation. This is what that protocol looks like

in this algorithm form. Can you see that? No, okay (laughs). So there's some more specifics, there's some more specifics of how that pacemaker needs to be programmed during scanning.

If they are pacemaker dependent, going to an asynchronous mode.

The ACC, AHA, and ACR are coming off of that very recent expert consensus statement, and they are saying patients with

MR-conditional devices should be scanned if that product labeling is adhered to, the protocol is in place, the personnel have ACLS, they have monitoring in place, and all that resuscitative and emergency treatment

can be done outside of the MRI area zone four.

Okay, here's the quick essentials

that every nurse would need to know in regard to these patients. What's the make and model of the device? What's the brand? Why's it there? What is its function?

What is the magnet mode? This is the pacemaker code that we just talked about. And so, this will also involve what is gonna be the device set at for the MRI? When was this device checked?

Who controls this device and follows up with this patient? I don't have this up there, but I don't want to negate this either, does your hospital, or does your institution, have a programmer have the programming equipment

needed for that make and model? Who can run that programming equipment? Why does this patient have this device? And if the pacemaker is turned off, what rhythm is this patient in? Okay, just for a little comedy,

"Instead of jogging, can you just set "my pacemaker to beat faster "for 30 minutes a day while I watch TV?" (laughs)

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