Okay, Bob talk to Nordstrom. I'll hand it over to you.
Thank you very much Julie and thank you.
Everybody for,
for being here today on this last month of May,
as they saying about,
we are here today to talk about some work that Dr Chad portals is doing and it's my pleasure to introduce him to today's webinar.
Chatter and his doctorate in biophysics at the Medical College in Wisconsin back in 2004.
You completed postdoctoral training, then at Vanderbilt University, medical center in 2006, but, you know, he stayed on there for a while, attaining the academic rank of associate professor in the Department of Radiology and in the Vanderbilt Institute of imaging science.
I was introduced to him at that point in time through doctor, Tommy ankle was a member of the quantitative imaging network then.
Naca Quarles research focuses on development and application of multimodal imaging methods for improve disease characterization.
He has a particular interest in expanding the clinical utility of bile imaging techniques.
chad's research has been funded, not only through the, but also through the.
He's involved in national initiatives, such as the.
The National brain tumor society, and, of course, the quantitative imaging network where he is now serving as the chair of the network's executive committee.
As you can tell, I could go on and on about Chad, but we want to hear from him right now. So, Chad, I'm going to turn the microphone over to, you.
Thank you very much able to share this and put it in presenter mode and see if this works.
Good to go looks good. Maybe you can switch the presenter mode and because we are seeing your preview.
Okay, that has 2 slides. Yes that's a, thank you so much. Perfect. All right.
Thank you all for this opportunity. I really like talking about a lot of the bio physics that goes into a lot of our works. So this is really fun. Talk to me. Now I look forward to doing this today. So, just as a 10,000.
Foot view primer, so dynamics, susceptibility contrast. And Mariah is a technique where we collect dynamic images. And during that acquisition, we inject a contrast agent.
And that introduces a signal changes as you see here typically a signal decrease.
And then we apply some kinetic analysis to drive maps that are that relate to them underlying. He wrote an index.
In this context, we're looking at blood volume maps, and that's just typically generated by looking at the area under the curve of these types of signals. And so that's just very high level. And in the context of your oncology is increasingly used.
It's certainly 1 of the, the most used physiologic imaging techniques.
In clinical practice, so, in the context of diagnosis, it can be used to do things like differentiate tumor grades and different types of tumors.
Identify high grade components within not enhancing glioma.
And it could be used to help guide surgery surgery for doing improved image, guided biopsies.
It can be used for dose mapping and radiation therapy. It's primarily using the context of numerology where it's used for surveillance and ice scans, particularly looking for treatment response and recurrent tumor.
So, then this is, as I said, this is probably the main application across sites is using profusion and.
To differentiate between treatment effects and to my recurrent. So this is a more violent hue just showing that in recurrent glioma you see higher blood volume and you see in areas where you have these tumor related are rather treatment related effects.
And this makes a lot of sense because if you have tumor, that's progressing, it's going to introduce angiogenesis and you're gonna have higher blood volume and profusion.
Another strong application for profusion is as a therapy response biomarker this system work by as part of an acronym trial, looking to see whether or not profusion changes early profusion changes could predict patient prognosis.
And so, in this figure, they compare survival, just looking using standard, conventional arise. So, just looking at the degree of enhancement. And so if you have.
You know, different patient populations that have stable versus decreasing enhancement. You see that there's no difference in survival. However, if you look, this is a data that's acquired 2 weeks after the 1st dose.
You can see that patients who have a decreasing blood volume, have a much, much more improved survival in those that have an increasing.
And so there is still and continues to be a growing interest and and it's more it's more frequently incorporated in the context of clinical trials.
So, just a bit of background on the physics of because this sort of leads to some of the challenges that people have faced really over the last 2 decades of how to get reliable, profusion estimates.
And so, when the contrast agent flows into blood vessels, it's going to it's going to have a number of different interactions 1st is going to interact with the water within the blood vessels. And this is going to reduce the T1 within the blood vessels.
If the blood brain barrier is broken down, as it often is, in the context of brain tumor, that conscious agent will leak out and then it will interact with water in the extravascular extra cellar space.
Typically, these contracts agents do not get inside cells outside the blood vessels. Now, what we're really mostly associated with the T to start effects.
And so, when contrast agent is compartmentalize if we look like, in this example down here with an intact blood brain barrier, you get these magnetic field renovations that occur around the vessel, and so will defuse to these, these pill filled for innovations.
And this will cause the phasing and lead to shorten T2 star. And so these are considered a through space or effect.
Now, when the contrast agent, it will also compartmentalize around the cells and this can then introduce additional magnetic field innovations, which can further decrease the teachers star.
And caused a different signal changes and so just to help you visualize that if we consider here.
2 different common effects that we can see and brain tumors. We would typically say, T1 leakage effects are T2 star leaf defects. So on the left here is T1. So, what we've plotted here is the change in the teachers, star relaxation rate as a function of time.
If we look at, forget my color behind this top curve here, I wanted to say blue, but that can be wrong. You can see that you would get the expected pharmacokinetics with Washington and washes out.
However, when the leaks out, and we have these strong T1 effects.
Then the delta star, this function goes below baseline.
Uh, now, when we have T2 star effects, this can occur, because of a strong residual contrast within the blood vessels.
Like, if you have very, very high blood volume, or if the conscious agent leaks out and surround cells, as I mentioned earlier, this can increase that Delta function. And so, as I said, you know, the integration of this is just blood volume.
And so if you integrate this, you're going to greatly underestimate bloodline, integrate this, you're going to overestimate blood volume. And so this has been known for a very long time to confound the reliable estimation blood volume.
So over 20 years, people developed a large number of techniques. To overcome to reduce the influence of T1 and teachers, star effects.
The 1st thing is just to optimize your acquisition parameters primarily in the flip angle, and you want to do this to reduce T1 and increase tq star sensitivity. Another approach was to completely eliminate T1 sensitivity through the use of multiple echoes.
And we'll talk about that in a bit.
A, very common technique to reduce T1 effects is to introduce a contrast agent preload. So, this is just inject an additional dose of the conscious agent about 5 to 7 minutes before your profusion injection.
And this has been used for quite some time and has been shown to be very robust and doing things like, looking at trying to identify high cellular components within tumors based on blood volume.
Finally, we can try to correct some of these leakage effects in the use stuff, pharmacokinetic models or just any type of signal analysis. But 1 thing that you can imagine is that with all of these options available.
We end up with a lot of we get end up with poor reproducibility of due to the lack of standardization and this was summarized well, on a study by Patel in 2016 look where they did a meta analysis to look at. How.
Parameters varied, and how that influenced thresholds for, and this is for differentiating tumor recurrence from treatment effects. And so, you see, you definitely have a range of and flip angles and you.
But then this gives us then a range of thresholds, that range of the order from 1 to above 2 and for the same exact application. And so this is underscored really the motivation. And the need to standardize this.
Now, about this same time, and for years of it before this, my lab has had a strong interest in really understanding the bio physical basis of what drives.
Uh, the contrast or the signal changes that we see in and so I had a graduate student the time, uh, Nate similar you'll see his name all over these slides because he continues to be part of the lab. He was aggressive and a post doc. And now as a senior staff scientists with me.
And he hates when I say this, but this basically persons, 5 years of his life, this is his graduate student work.
Where really we wanted to develop a computational framework to derive simulated signals that match what we see in brain tumor patients and so this involved coming up with 3 D tissue structures.
Deriving the math that we need in order to compete magnetic field and then converting that into signal changes and in doing that so that we can in such a way so that we can vary things like the concentration inside the blood vessels and the extravascular extracellular
space.
So that we could really begin to dig into this. And so we had just publish this.
When I get what I'm going to say to a bit, I'll just say quickly. We, we really developed this at the same time. People were really interested in standardizing protocols and then provided a unique framework, but just to dig into this a bit more. What really.
Made this computational approach really attractive? Was that 1st off? We dug into what.
What really influences signal in terms of both the vessel architecture so we did things. I've looked across different vascular tree networks. You see how that influence the derived Delta our 2 star values if we change, I can branching angles.
Between the vessels, how would that influence our, our profusion parameters?
And so we went through that process and then,
I think 1 of the key key elements that really enabled us to develop something unique,
was looking at the influence of cell architecture or self,
sell features on the signals.
And the reason we had to go down this road was that we could not reliably replicate patient data if we did what people have done for,
for a very long time in DSC,
computational work,
which was just to use randomly oriented spheres,
which from basic concepts of principles those are really useful for understanding things,
but when you try to simulate patient data,
it just it was insufficient.
And so, if you look at the curve here 1 problem, is that the magnitude of the teachers star effects that you see are very small. They don't then we could never get teachers started leakage effects with these random spheres if we did highly ordered sphere.
So these are facing or Cubics fears, you get even smaller T2, star effects, but you can then go out to higher cell volume fractions, but narrow. These really enabled us to do that.
And so what we have to do, then is begin to look at a structure of cells that would enable us to have a lot of heterogeneity as you would expect inside tissue. And this enabled us to get those high sale volume fractions as well as have larger teachers star effects.
And this really was a breakthrough in doing and simulating a very well known effect,
which is the fact that I should not affect but phenomenon that we see in the clinic,
which is brain metastasis often present with very strong TT,
start leakage effects.
While lymphoma, primary, Central, nervous system, and then FOMA have very strong 1 effects and histologically. We know that 1 of the things that make them very different is their cell size.
So, brain metastases have a, a, a radio about 15 my crimes and then from about 5 my friends, and when we, when we use these elipsoid and stimulate these, we can see that Natalie, you're able to re, capitulate those sort of expected changes.
So, here, the blue curve is where there's no conversational leak, but when you allow for the week as a contrast agent strong, keep you start effects for metastases and strong 1 effects for lymphoma.
So, this was really encouraging and so then this is about the time when these different societies we're trying to come up with a way to standardize. Instead of the roadblock they encounter was.
We really have no way to validate.
Across a wide parameter set what what the accuracy of the blood volume measures and so we were asked to use this computational approach.
In order to simulate all the different parameter combinations and identify what is an optimal way to collect this data.
And so, this ended up leading to what is now the DSC, where I did to the reference object, which has been used in a number of and challenges and other studies.
And, you know, this, this doesn't really do it doing a lot of justice, because we just have a lot of tomorrow. Fox holds a lot of normal, appearing, white matter of oxygen some pixels. But really what goes into this is what is really interesting to us.
So, when we 1st started going down this road.
We quickly realized that we couldn't do what we've done in the past for a simple characterization studies,
where you would just say,
let's very systematically vary blood flow or blood volume or leakage effects and see how that impacts the delta star of the signals.
Because when we did that to try to optimize parameters, what we found was that they say we used a uniform distribution of K, trans values for how much blood brain barrier permeability there is.
Say we used a uniformed distribution that would give us 1 set of optimal parameters.
Well, then, let's say we use a gallery of distribution for those same K, trans values.
Maybe we get a completely set of different, optimal parameters and so we realized that what we needed to do then was to train the, the, the computational approach.
To recapitulation signals that we see in patients, not just generically, but for specific combinations of parameters. So that if we have things like.
So that we have pair combinations of blood flow, blood, volume T1, star, diffusion K, Tran, cell size. They're, they're, they're.
Their geometry with respect to each other, their distribution and spacing all those features. So that on a on a basis, we could match what we see in patients and so it took 40,000 pixels from a database of DSM right?
Data and we did fix a lot of the parameters, or at least allow them to vary within publish ranges of things. I, profusion and blood volume had been measured with other modalities. Like.
And then allow the free parameters to vary until we could match the data. And so we won't be.
Really want,
you know,
we didn't want to just do like,
you can see the video and the silicone data seems to match,
but we really needed a way that for every single structure that we derived,
we needed to be able to compete the thing that reflected both the T1,
the teachers star contributions to that signal and what we came up what we use is what?
Sometimes, using the literature for patient use, which is the signal recovery because that reflects both the signal drop as well as the recovery. And this tells you about the magnitude, the dynamic magnitude of the T2 star effects.
And so in this training phase, what we really wanted to see was that.
That the 2, the silicone and then vibo data match, which you can see it does. Of course, you can, this is just like any kind of not only your fitting and you can as long as you have enough free variables, you can match anything.
So, the key really was to then say, take a completely different data set that was acquired with a completely different contrast. It had its own consciously activity. It was given at a different dose.
It included different acquisition parameters and say, can we now predict that data? And the goal here necessarily wasn't to be.
Exactly correctly, there's a much smaller database and our training database was enormous and the era that developer was at this time about 10,000. so we knew that our range would probably be larger. And so that really wanted to see basically could we capture all?
The T, want to teach star effects and so that's what you can see is that this data set is a subset of what we see from the computational work. Similarly, you get good agreement between the results.
And so we knew at least now that we were really capturing the full range of T1 anti 2 star effects, and could begin to use it now optimize acquisition protocols.
Now, I'm summary summarizing a lot. A lot of work, we stimulated thousands of parameter combinations.
But we really focused on just to sort of present this in an efficient fashion on a number of different protocols.
Well, the had published a list of recommendations that included a moderate flip angle around 30 and then they gave they actually gave flexibility from preload dosing.
And again, that was because of lack of data to know what the actual accuracy was. And then we also wanted to look at protocols that were considered compliant.
So, basically, where you could collect a post post Gad, T1 way to image after a full dose of contrast agent and all of these protocols enable that. Because these will be the ones that would be clinically relevant.
And so we look at this, of course, at 1.5 T3 T. I'm sorry, I should say this. So, this is this dosing scheme here is the amount of Pre load as a function, or related to the the.
The size,
I'm sorry with respect to his standard dosing so 1,
plus 1 protocol means a double dose was given in 0 plus 1 is no Pre load with a standard those given for the primary profusion injection in terms of looking at how accurate these these different protocols where we use can coordinate
correlation coefficient and for precision the coefficient of variation.
And so the 1st thing that really was worth noting with this, is that.
With this with this approach, and I've been used for quite a long time, this double dose strategy we see that we get very strong agreement and great precision at both field strengths using this approach.
However, if we now give people the option of doing different.
Pre load, dosing and bolus, dosing combinations that you actually take a pretty big hit.
In all of these different options, particularly if you go down to something like no preload and the accuracy and precision depend upon filled strength. And so you can see how this would then impact the use of profusion in clinical trials.
And in fact, there had been.
Disparate results in different clinical trials, and you could, you could likely attribute it back to the fact that some use this double dose strategy. Whereas other used these other different dosing protocols.
Now that the more interesting part of this result.
What's the fact that if we use a low slip angle with that?
Feel strength optimized echoed time.
We actually get really good accuracy and precision across any dosing strategy, including no Pre load dose and this was actually really this was really exciting because then this gave us a, an option of eliminating.
That Pre load and reducing the amount of Gad given by half.
Which, of course, is useful, clinically, particularly given the concerns over, giving a lot of gotten lineage and just as well as the practicality of having to give, you know, doing multiple injections in these patients.
And so Kathy mind leaving Hugh Jerry box him and myself,
we put in a grant to do a multi site trial to validate whether or not this new low flipping protocol could what we get more established,
double dose,
moderate or intermediate flip angle protocol.
So, this is just a comparison of the maps.
Uh, in a glioma patient, showing the local angle, this is no Pre load just a single dose.
Whereas and this is the double dose protocol, and you can see visually that you get very good agreement, particularly in this, in the rim of the tumor as well as around the rest of the, uh, the brain.
If we look at this across all patients, so this is 43 patients you can see that we get a strong lindscott recordings correlation coefficient between the 2 protocols again, just highlighting that.
And then validating and vibo that Watson simulations predicted we actually actually played out to be true.
In a separate cohort of patients we've also looked at the box. So looked at this at a box of why it's analysis.
So, comparing the corridors correlation coefficient between different parts of the brain, because this sort of shows you or gives you at least an indication of what it impacted radiological assessment, or view of these of these maps. And you can see that.
You still get very strong agreement. At the level, which is, which is fairly impressive for any imaging modality.
So, this ultimately led to what got published last year, as, as the consensus recommendations this is from a group of people at the National brain tumor committee. They was called the jumpstarted embracing reductive element, collation, imaging, standardization, steering, steering committee.
It was a a large number of people that really invested in the brain cancer imaging.
And it basically came out and said, either do the double dose protocol. Or if you have if in a single trial, you want to use 1 or the other. But for routine clinical practice.
That they low flip angle option is is certainly viable.
So, where are we going next? So, 1 of the things that we've learned through our interactions with the is that, as you can imagine post processing steps, influence, accuracy and reproducibility. So, Laura Bell.
Uh, at Barrow did 2 studies where she used this to look at how these different post processing steps across, like, 10 different sites impacted CBB reproducibility and of course, it had a major impact. And so.
What we didn't want it to do next was to develop a, and what I should say, what we're doing next is developing a park to validate software to do analysis. And so we can't the 0, what we learned from those challenges was that.
It's quite a challenge to develop the icons that can be read into.
All these different software packages, but also a lot of the packages.
Make certain assumptions about the anatomic structure of the data you're looking at and so we wanted to develop anthropomorphic arrows that could be easily read into any software package.
And so what we're doing now to some new funding within age is developing these in silicone maps, where we're actually matching at the pixel wise level data from our 0 to data.
And so this is really very little effort. You can see that we get fairly decent CBB maps based off the computational results that national. We see.
In vivo and so right now now we can use this com to read it into standard profusion software.
And really, the key here 1 of the key things here, is that for all the challenges that we've done that done thus far, and for the initial optimization stuff.
We always compare the ground. The ground truth is always the blood volume that you would get an intact tissue. So if the blood brain barrier wasn't.
Disrupted, and because that's the that is the primary source of discrepancy in terms of the accuracy and reproducibility and how people how that's handled. Well, in a true benchmark you have to think about the ground truth that you have.
And in this case, that's what makes it kind of unique is that.
We can never really get to the absolute CTV estimates because of what's known the contrast agent vascular activity.
And that's a parameter that, that scales the measured signal change by and sorry it skills that the local concentration concentration by this scaling concept, to give you the change in the to start.
And so what we had to do for the then was to compute.
That vast activity for every box, so that we could then use the input, blood volume values as the actual ground truth.
And so this is what enabled us then to have a true Benchmark. So this is like the 1st, very preliminary results where, you know, where we said, okay, let's just look at the case of intact blood volume. So, this doesn't require any special post processing using this new ground truth and you can see that.
So, this is the best case scenario because this includes no, it's not going to be exactly 1.
But you can see that we do fairly well if we look at data that hasn't been corrected for leakage. We get a really poor agreement if we apply what's known as the cough correction we get very, we get values that are very close to ground truth.
And if we do something that was used and used to be used quite a bit, was just fitting these curse again. Very functions. You see, we get very poor agreement. So, this really gives us a way, then to dig into the post processing methods.
As well, as to validate software, so I'll spend the last few minutes I'm looking at opportunities for improvement in where we're kind of going next with this field.
1 thing that we know is that if we use the and then do things like change, the T are really low value.
And the reason we were doing this is because we have an interest in a simultaneous multi slice or multi band acquisitions, where that enables us to get very high temper resolution. Only do that. What we find is that are low flipping a sequence.
If we don't do a Pre load, it reduces its reliability. However, if we still use these double dose strategies, we still maintain the accuracy of it.
But once it's really indicates is that ultimately with single echo methods, the fidelity of the CBB maps, and the data, the data itself will depend upon the local T1 and TT star dynamics and the acquisition parameters.
And this becomes important, both as an opportunity and it also, as a way to make the data even more useful.
Uh, when you think about how we're using profusion in the clinic.
You know, we develop these methods to maintain or to give us a highly accurate, which is very useful for things like clinical trials.
However, it raises an interesting question of why don't we develop specific protocols are very specific, clinical applications. And this really goes back to this. What I mentioned earlier about the differences between like, metastases and lymphomas.
Where are they metastasis? Very strong, teach you star effects gbm some more in the middle and lymphomas had very strong T1 effects.
And so, in the context of, like, single lesions in brain mats, it's very often it would oftentimes be very useful to know whether or not that the patient had a dBm or a metastasis.
And you could do that if you could optimize protocols and parameters to differentiate between these 2 just based upon something like TSR. And so, 1 of the things that.
Sorry, that was just showing the estimation of was 1 of the we discussed a few years ago, which had this. I asked him a few years ago, but if you, if you use the sorry, let me pull up some.
Color here, if you look at the, uh, the optimized or the consensus protocol, this low flip angle.
30 millisecond, echo time protocol and you try to differentiate between.
These 32 more times you basically can't do that however, if you can, you can optimize. So we did that we use a Darrow to do this. You can optimize the parameters to differentiate between these different types of tumors and all of this.
Just to say it won't belabor this just to say that there could be a setting, particularly in the novo setting, where you could optimize parameters.
In order to the answer, these very specific clinical applications at the expense of something like the accuracy. But maybe that's not important in those cases.
But this also really highlights, then the last part of the talk, which is transitioning into multi echo acquisitions. And the advantage here is that we can use different echo times to really fine tune or contrast.
So, in the case of, like, the clinical application you could, because you have multiple echoes, you can use all of that information to make those decisions between differentiating the different tumor types.
But another nice thing, and people have known this for a long time with multi echo acquisitions is that you can completely eliminate T1 in T1 leakage effects.
But you could also quantify T1 and 2 star effects, and what this enables in his simultaneous. And this is just some work that we did a number of years ago showing that you get. You can get both data from these multi echo acquisition.
This was comparing it to a standard protocol and then the Dr K, trans values, they are really installing agreement.
And so we've implemented this clinically at Barrow, and we've been testing it for a number of years. Now, this is idea, or a validation of multi echo and 1 thing that this was really interesting to see is that across build strengths.
And across all of the parameter variations that we have and dosing protocols, we have very high accuracy. And what that showed us, was that these multi echo acquisitions, the couples, the accuracy of your profusion metrics.
From both of the parameter variations and the tissue property variations.
And so, in the cases of, like, and lymphomas, because of the very unique T, when I teach star properties.
You're going to hit different accuracy of the CPV values, using something like the consensus protocol. However, if you do multi echo protocols, you're going to maintain that accuracy. And if sites are using different parameters, it's actually not going to impact.
Your CBB values this is in patients so this is in 60 patients again, just comparing this. No Pre load, multi Eco approach to that double dose protocol that I've mentioned several times. You can see that. We see really strong agreement.
And 1 of the things that we're really excited about in that same grant is doing the benchmark. We're also developing a new 3, multi, echo set, spiral approach and the motivation for doing. This was to get rid of a lot of the artifacts that we see. This is the 1st patient that we scan using.
This is just preliminary data for the grant, but you can see an API. You have the usual artifacts, however, with the 3 multi echo acquisition, you have data that is geometrically more analogous to what you've seen the anatomy.
And so for things like radiation planning and nearer surgical planning, this will provide much more robust results.
So, I'm going to spend a little bit time on just the next few steps and where we're going with this.
So 1 of the things that we learned early on with our simulation work was that the microstructure really influences the data that we get. And so if we look at a different phase and say a vascular phase, a mixed phase.
And an equilibrium phase, where the conscious agents, the equilibrium between the plasma and the extravascular space, you can begin to see the influence of microstructure. So, this is the, this is a 2 D slice of that structure. So we have vessels and cells during the plaza phase.
The magnetic field predominantly surround the blood vessels during this next phase you can see both.
The blood vessels to some degree as well as the cells.
However, what's really interesting to us is that in this equilibrium phase, all you can see is the cells.
And so it says that if we look at data in this phase, what we're really sensitive to with this, the right data are the actual underlying cells.
And so we begin to explore this and what we find is that the teachers star effects are very sensitive to sell feature. So this is looking at variations across sell size as well as cell volume fraction.
And this kind of takes us back to why we want to do multi echo acquisitions as well because with these acquisitions. Because we can quantify T1. Now, we know the contrast agent concentration from the T2 star. And by combining that with the teachers star, we can actually get a measure.
Of the contrast agent, T2 star real activity, and we call this parameter trade. This is the transitory, real activity at tracer equilibrium.
And again at this parameter, which has to now be quantified again variable is a function of cell volume fraction and cell size.
So this is something that we've been using, we've shown in animals that you can differentiate different tumor models we're using in the context of patients to do things like, differentiation of treatment effects and tumor recurrence and then also trying to use it to evaluate response.
Um, the last thing I'll talk about is the, uh, what is sort of an emerging trend it's been around for a little while, but is doing multi scale, profusion imaging by combining gradient echo and spin Eco, acquisitions. So it's been known for a long time.
This is some work from Jerry boxing that gradient Eco data is predominantly sensitive to vessels of all sizes where Hispanic with primarily sensitive to capillary structures.
And then if you combine this information, so the ratio of the of the delta actually started Delta are to give you maps and vessel size. And if you look at the dynamics of grading echo versus spec or data, you can actually tease out information about the underlying vessel architecture.
And so, this has been shown, theoretically, it has been shown, even in using some, some context in brain through your patients to look at things like treatment response.
And so typically people do that either with sequential grading echo and Spanish acquisition. So, 2 different doses of contrast agent.
Or they use a single gradient Echo, single spin echo. And the challenge with that is, of course, is you're still going to have the sensitivity to all the, the leakage effects that I've been discussing throughout the talk.
And so 1 thing that we realized was that if we can combine these multiple spinning grading that quite physicians with the idea of doing multiple echoes.
Then we can really this provides the most robust way for collecting all this different data.
And so this in the sequence, what we have is 2, it goes to asymmetric, spend Eco and a spin echo. And so with this, we can quantify the dynamic changes in T1, T2 and T2 star. And these are just the different echo maps that get produced.
And with this, we can extract total and microvascular, blood, volume, blood flow, main transit time time, heterogeneity the vessel size and vessel textural imaging metrics.
A DC parameters, like K transit V as well as these, these trait values that relate to the underlying celularity. And so this is something that we've actually now as part of the Q I. N.
are harmonizing this sequence and its parameters across all the major vendors. So that it can be used in the context of clinical trials.
The next 2 slides are really quick. I promise the where we're taking this next and this is data that hasn't been seen. I have to completely completely acknowledge this is a collaboration with Jerry boxmen. So, as a graduate student.
He had this idea and wrote down equations, but he never had the computational sort of framework to do it. But he realized that with our, we could actually tackle this problem now.
And so the basic idea is that we're trying to transition from going to vessel size imaging, where you're getting a mean vessels a voxel to actually computing the distribution of vessel size within a Voxel.
And this is this could work when using conscious agents with strong susceptibility effects like.
Because of how they shift the relaxation rate versus size curves, which is what's shown here. And so to do this, you could use sage or a guest file type CCO as to what's shown here.
And then, with a row, we can create a library, a library of relaxation rate versus size curves, and then use a linear model to derive the underlying message distributions. This is very preliminary results as of, like, 4 or 5 days ago.
However, it was really encouraging. It's the 1st time I was willing to actually talk about this.
So, we simulated signals for a different vessel population that had known waiting factors for different vesicle sizes and then computed the different.
Ran it through the linear model, and basically came up with these estimated and, of course, the underlying groundtruth expected values and you can see that. So these are the different vessel radiuses here these are the waiting fractions.
So, here, this particular vest vessel population had very a lot of predominantly very small vessels, but you can see that the estimated values are in fairly good agreement.
And the worst is the 1 that has the lowest volume or waiting factor, which is wouldn't be expected, and things that we have to explore with this just because of contracts and noise is going to be some limitations with this.
We are also taking this where we think this really holes a lot of opportunity incident. This was using deep learning methods because there's so much information in the signal to K curves that we're missing out on by using this simple linear model.
So, you know, I mentioned this, you know, vessel size imaging, it's just the ratio of these 2 things and where we are taking this next with the stage data is actually applying this to do cell size imaging.
And this is our preliminary results showing how, if you look at that equilibrium phase, and just compete the same ratio, and look and plot that as a function of cell radius.
You can see that you get a fairly strong relationship and so may be able to actually the cell cell radius within a pixel.
With this sage information I knew, I flew through those last slides is probably the most interesting part of the talk, but.
Um, I will acknowledge everyone that's contributed to this project over a number of years, both at Barrow and originally in Vanderbilt, this project started there as well as external collaborators and a few of our clinicians at and Barrow.
And, thanks, of course, the funding, particularly from the that's funded this work for a number of years.
And without I'm happy to take questions.
Wow questions, you know, uh, Chad you've confirmed in my mind, that magnetic resonance imaging is indistinguishable for magic.
You threw a lot of stuff at us. Let me start by asking a very simple question. Your time graphs that you showed you showed several of them in the, in the course of your of your presentation.
Were they collected from a single box or a single pixel or how does how does actually does that? What does that representing when you have those? Those, those time curves.
Yeah, so most of them those are single box full time quarter curves.
Because, you know, all the analysis is, is meant to be done and to create maps and things. So yes, those are those, those were from single pixels.
Then what the obvious next question is, what was what's what's the difference looking in the curves to the next.
Box all the next page, how does it vary with with spatial.
Point of view. Yeah, no, there is in fact, a lot of heterogeneity and this is that's 1 of the, the strong justifications.
For using things like blood volume, right? Because if I just look at traditional post contrast, T1 enhancement, you see a pretty much uniform enhancement across the tumor.
But with something like CBB, you see a lot of heterogeneity, just because of the head of the baby of the tumor.
And so that's how it becomes useful for things like cervical guidance or radiation therapy planning.
Thank you other questions. Yeah go ahead.
I can't tell who someone has a hand up. Yeah.
Oh, can you hear me now? Yes. Oh, great. So thanks very much for the excellent talk. I just a general, I guess kind of a.
Question the earlier phase of the talk was sort of describing how you could take into account a lot of the environment and.
With a goal of improving the fidelity of the CBD measurement. In other words, the primary goal was the was to stabilize and CBB by taking into account these other microgram and factors like the cell size and so on.
But but I gather that with that conventional data, it wasn't your preference for measuring those micro environment factors. Is that right? So, in other words, the.
The matching process to the dictionary that you have, and then silicone sort of simulations was it was good enough to capture those features or separate them from the CBB effects, but not good enough to serve as a measurement of them. In other words.
Yeah, and I get that's exactly right. I think, you know, where you're going, you're looking at can we actually then use this matching procedure to try to derive those parameters.
Right, yeah, and that that is our currently our holy grail is, that's the next 1 renewal is trying to particularly with the sage data, and using a fingerprinting type approach to derive as much information as we can all along the time.
Course. And that we get, we certainly have some ideas on that and that's really where we want to take this because that is sort of that Grant's been funded for a number of iterations.
And what we, the whole theme is basically trying to extract out as much information as we possibly can without adding time to the exam.
And so that's definitely the next direction of that is really leveraging both the acquisition as well as that the to do that.
And I see that Jean leaves on the call as well. She's actually Georgia skewed technology. She's been instrumental in this process because we're, you know, this is going to.
1 thing this is like, the perfect setup for a deep learning type of environment, because we can simulate as much training data as we want. And so we don't have that problem. And so it really creates a really unique opportunity to do those kind of things.
And it sounds like, as you said, and just the final comment is that as that next phase will, we'll call for.
Acquisitions that go beyond the current standard. In other words like, yeah, you're saying, right? Right yeah. You can't do it with single echo. Yeah, we, we thought about that and there's, there's too many parameters right?
Thank you doctor Sylvia has a question Dr silver if you would like to unmute your line and.
Ask the question directly. He says he doesn't ever Mike.
So please, let me read the question to you. Other results, you obtained Translational invariant quantities. And the 2nd question is, did you have to perform any spatial correction for the 3 D3 T acquisitions?
Spatial, um, for the for the so that is the 2nd question. I can understand that 1 more clearly the.
For the API data, we are looking into options or ways to correct that but know that the anything in the clinic it was just what was off the scanner because that's what they used clinically everyday.
Certainly, as I mentioned a focus is to overcome a lot of the spatial artifacts that you get with.
But now that at least for the, at least for the validation studies, there was no spatial corrections applied and they would have been present. Any kind of distortions would have been present in both data sets where we were comparing the single dose versus double dose acquisitions.
Uh, says Thank you for the answer.
Okay, there are no further questions. I, I, this is Barkley Kemp I have a question.
Okay, thanks. Oh, well, thanks for the excellent presentation. I really enjoyed it. And this kind of opens up though, like a huge window for keep to style weight it or T, to store maps.
That's currently kind of problematic for.
Uh, fingerprinting, uh, you know, uh, uh.
Images I have a quick question.
So when you do these experiments, do you start with some kind of idea on what the T want T2 values or T, to style values would be in certain areas.
Do you measure like a T to star? 1st?
In a way, so for, you don't have to do that.
Uh, because you can directly quantify the TT star is these are multi acquisitions so we can do that Pre contrast.
However, with that said, you can't the same is not true for T1 so some of the more advanced stuff we're trying to pull out anywhere we need DC information. Just like a traditional DC exam. We have to do a Pre contrast to a map and.
You know, people have done that with multi flip angles or multiple inversion times and so yeah, so, but we, but we don't have to do a, we don't make any we definitely make any assumptions about what the values are before.
And we do, we do measure them either using a separate scanner as part of the scan.
Uh, okay, well that's that's great thanks. And, um, for the artifacts that that comes with.
With when you use spiral,
it kind of which you say,
that it resolve the problem or is it just like,
I,
because I was like,
a over sampling and in a way.
So, it kind of, um, uh.
Average doubt that the artifacts what what would you say about that?
So, in the 2, the acquisitions, this firewall is, it does have improvement and caffeine's mind has actually published that as well with a dual Eco spiral that they called spice a number of years ago.
And they showed that,
I will say the 3,
the implementations are a lot better,
because a lot of times you trade off some of those distortions for blurring and so we still get it's still not as clean as I would like.
And that was 1 of reasons that we were pursuing the 3 D acquisitions.
All right? All right. Great. Thanks.
Can I answer that last question? And then from Aaron, in the text box? Yes. Please. Go ahead.
So we actually have a paper on that from a number of years ago, and in the context of brain cancer and basically, there was the, as you can imagine, the answer depends upon what how you acquire the data.
If you acquire the data using traditional, just conventional parameters there really is in poor correlation between K2 and K trends and it's because of what they're just.
By bio physically related to.
So, there's not, there's not much of a comparison or a correlation between those. I think the only way that you would get a strong correlation is, if you really shifted.
The acquisition parameters in order to maximize that correlation, but with the current acquisition protocols, there's not much of a correlation.
And so it's probably a bit correct to call K2 utility. It is certainly some kind of extrapolation concept but as you can imagine, so you had a strongly T2 star weighted acquisition protocol.
Then then, that cake is gonna be heavily dependent upon the tumor cellularity and have nothing to do with permeability.
Oh, that's a great question as well. So, is the relationship between the delta actually started the cell function. How much is that related to the self thing versus just the trends that create activity so, that was really, I didn't really show a lot of those values.
I didn't highlight those, but if you look at the ttb activity of the contrast agent, and on the order of, say, 3 to 5, right? Whereas the teachers star due to the cell effect, can be on the order and brain tumor patients.
You know, 30 to 80 and so it's, it's quite a bit higher and that's because of when you're when it's compartmentalize around cells that cell wind fraction is much higher than what you would see for, like, blood vessels.
I think we should move on. Really? Yes sounds good.
Uh, next on our agenda is an update from the canceled imaging archive and I'll hand this over either to John fragment, adjusting Kirby.
Okay,
so just a few updates today the 1st,
1,
is that the cancer imaging archive is partnered with the American College of radiology,
data science Institute to help link data sets to the various use cases that they've put together.
So I'll just show you briefly here.
Uh, this is the, the, our website, and they have basically defined a whole bunch of different.
Use cases that are on their site and and if we just take a look at this breast cancer risk assessment 1.
They go through the value proposition and narrative and workflow, and then define the various types of inputs and outputs that you might need in order to address that use case.
And then now, what's what's been added at the bottom is they've gone through all of the different data sets that are on and tried to identify data sets that we have that could be used to try to.
To answer these questions so we're, we're excited to see them taking advantage of our data sets for that.
We also had to 2 new collections published over the past month.
There's this anthropomorphic abdominal phantom scan data set, which has 3 different configurations that they shared for the Phantom.
And then we also had this Chinese mammography database, and this one's almost 1800 patients from China with benign or malignant classification labels. And then 749 of those also include molecular subtype labels.
And then the last thing is we've for a while, been keeping a registry of what we call data analysis centers. These are basically.
Other activities that are building off of data,
either by providing access,
start data sets via the API,
or creating a copy of the data sets and then adding other tools to provide additional functionality beyond what we actually.
Do with TCI itself, we added this new 1 gummy just recently.
And this tool is marrying the CBS mammography data set that we host and this enables you to find patterns and imaging data sets and assist with providing explainability for machine learning models.
So that's all I have thanks.
Thank you, Justin um, moving on to the update, uh, from the imaging data comments just a reminder.
The, here's the, your ll where you can find the imaging data comments uh, it is a cloud based.
Image repository currently, it hosts some of the tca collections.
And the number of collections hosted by the imaging data comments will increase.
I would like to draw to take today your attention to,
uh,
some of the capabilities that the imaging data comments office,
the imaging data comments utilizes capabilities of the Google health care API and extract all the contacts from the headers and stores them in a single big query table,
and,
you know how many contacts there are,
and you can imagine the size of the table in addition to the data stored in this big query table.
There are also tables for non data,
for example,
for digital object identifiers,
all collection,
that level data and for the license that is associated with each of the collections,
you can make use of these publicly available data and how to get to them and how to query the data is explained in the imaging data comments documentation
the U.
R. L. is but much easier found when you go to the portal homepage and simply click on the documentation link.
The imaging data comments is in preparation of their next data, release more of the valuable collections of yeah. More of the collections from tca were imported and will be made available for cloud processing.
And I will let, you know, on this call once this data release has happened.
In general, the imaging data comments team is working towards the production release in the fall of 2021 when, uh.
The portal will have a larger amount of data and a larger set of functionality.
Right now the minimal viable product that's available to you at the L. I mentioned above is like a prototype and we would like.
For you to provide your feedback, so we can build in the features that you feel are valuable into the upcoming production. And subsequent reduces.
Are there any questions either for Justin for the tca update, or for the imaging data comments update.
If not.
Then I'll move on just to a number of announcements as always, uh, invitations to the webinar will go out via the Google group.
But lately we have also been making use of the email list that the communication offices have at their disposal.
And we are very happy to see so many people joining us.
On these calls information about the webinars will go out.
In these emails, but they can also be found on the Vicki the, your elders listed here recordings from the calls calls are also posted on the same wiki page.
Our next call will be on June 7th, and we are going to hear 2 reports about medical imaging. The identification initiative.
The July call will be canceled because it's the 1st, Monday of the month and it happens to be this year the holiday after the 4th of July.
And the following call will then be on August 2nd, and we will let, you know, in advance who is going to present at this call.
And I'll hand everything now, back to Dr Nordstrom. Well, thank you for all that Thank you for being a part of this today. And, like I said, we'll be back together in August.
I don't have anything more to present.
Okay, thank you. Everybody for joining today. And I will post as soon as the recording is available on the wiki.
Wonderful. Wonderful. Thanks. Everybody. Thanks Chad. Particularly.
Happy to do it take care.