And here is the agenda pop I leave it up to, you.
When you want to start well, no time, like the present.
Okay, I want to thank all of you for taking the time here and the month of February Groundhog day is behind us and spring is around the corner.
I hope from all of this, uh, we have an exciting agenda today, but before we begin, I'd like to, uh, advanced to the next slide and just take a moment to make a comment.
Yeah.
Yeah, yes um.
We recently learned that Dr passed away.
He died in January of this year.
At the age of 45 he was.
He was diagnosed with Glioblastoma back in February of 2020, and he bravely battled that disease for several years.
Uh, while he endured a lot over that time, he accomplished so much more for us. We knew that she has a strong, strong work ethic and for the fact that he always learned new things.
Ashish came to his bachelor's degree in engineering from the Poon job engineering college in India, a master's degree in computer systems science from Louisiana State University, and a PhD in computer science from the University of Southern California.
We told that he worked at a high state university 1st as a post doc and then as a professor before relocating to Atlanta in 2008.
Well, he was appointed professor, biomedical Informatics at the emery University School of medicine.
Ashish excelled at his work authoring publications and designing cutting edge technology tools.
He was a respected number of the quantitative imaging network for many years, and he always mentored and helped others within the, and was known for being a team player went above and beyond the call, the duty.
I can also say that he was an important contributor to our book on quantitative imaging in medicine.
A complete, uh, obituary, it can be found at the link.
I've been told that donations can be made anywhere, but especially if the Windsor Cancer Institute at Emory where he was treated, or it's the Glioblastoma support network,
which helped Glioblastoma patients and their caregivers.
It goes without saying that that she will be missed in the field of imaging and Informatics.
Next slide.
Turn the page, not forgetting Ashish for what he's done, but to move on to the fields of imaging and Informatics and move it forward.
I'm sure he would have wanted it that way. It's my pleasure to introduce.
Dr Michael make that gray who received his education from the University of Washington in St Louis, Carnegie Mellon University in Pittsburgh and UCLA, completing his PhD in biomedical physics.
A document that gray is now professor in the Department of radiological sciences that UCLA and performance research in the areas of computer tomography image, acquisition, physics and analysis.
He uses image processing and machine learning to analyze radiological images. He is a current director.
Of the physics and biology and medicine, inner departmental graduate program at UCLA, and also serves as the associate vice chair cancer for research and radiation safety.
He certified by the American Board of Radiology and the field of diagnostic medical physics and as a fellow of the 80 P. M.
as well as the fellow of the, and has been investigator in the long imaging database consortium or the project many years. years
He's a member of the physics subcommittee of the National lung screening trial and, and it has also been a valued investigator in the quantitative imaging network.
He also served on the Physic subcommittee at the accreditation program.
A personal note, it's my relationship with Mike again, back in the days of ca, big. If you remember that, and continued through his extraordinary work in the quantitative imaging network.
He was also a valued contributor to our book on quantitative imaging and medicine and it's my distinct pleasure to introduce my dear friend Dr Michael industry to today's presentation. So, Michael, please.
Thank you Bob and I also appreciate the remembrance of who I also met through.
To see a big and the, um.
Networks and thing, um, NCI for both of those opportunities and not just the work, but the opportunity to meet just outstanding individuals like yourself. And, uh, let me share my.
Green here, um.
I'm known for long titles and today is the exception. Um.
And Bob asked me to talk to something about reducing dose reduction and, and I wanted to talk about its effects on quantitative imaging and machine learning algorithms and just walk you through.
Some observations and both of where we currently are, and some potential solutions for some of these, um, some of these issues.
So, uh, Here's my disclosures.
As I'm sure you all know, is, um, just a workforce and clinical applications and radiology.
And especially in the field of cancer imaging, it's being used for screening, such as for lung cancer screening diagnosis. We often use it to characterize suspicious lesions.
And to assess treatment response, um, specifically, um.
Looking at response to therapies, um.
A lot and we'll talk a little bit more about these as we go through, but.
Simply some of the treatment response assessments are just did a lesion, get bigger or smaller, but we're all interested in looking beyond that.
And seeing if there's other properties that we can extract from imaging.
Um, uses X rays, which are organizing mediation and there has always been concerned since the invention of about radiation goes to patients, but those.
Concerns have elevated over the past probably 10 to 15 years.
Primarily driven by some unfortunate incidents, uh, back in the late 2 thousand's of 2008, 2009.
And that led to a number of developments, uh, that has driven the dose reduction.
So, so, city manufacturers in response to some of those introduce a number of dose reduction technologies, and these are exquisite technologies that are commercially available on most scanners that are out there today.
The 1st, 1, I'll mention is automatic exposure control and that's where the output of the scanner is adapted.
To the size of the patient, and to the anatomy, this has resulted in substantial dose reduction to smaller patients and specifically.
To pediatric patients, um.
Another technique is advanced imagery, construction techniques.
Uh, their goal is to reduce noise while simultaneously maintaining resolution. You often hear these described as intuitive imagery, construction techniques and more recently, manufacturers are offering what's referred to as deep learning based reconstruction techniques.
Uh, more efficient detector systems have been placed in many scanners in the last 10 or so years. Um.
The materials and the construction of the detectors has changed to make more efficient use of the X Ray photons and therefore.
The reduces the need to send so many X, Ray photons and and reduces the dose to the patient while maintaining image quality.
And more recently, um, last November.
Sorry, a year ago November Siemens introduced the 1st commercial and approved photon county city. I believe that g has 1 in in beta.
And I believe that at least Canon and I, I think Philips is also working on proton counting. So you may see this from 4 major manufacturers in the not too distant future.
And this uses a different detective technology, it's a direct conversion of X rays to signal. And that requires even fewer photons.
And we expect those reductions from those technologies, these photo county.
Use this more direct conversion the conventional.
Views what's referred to as energy integrating detectors and often actually photons. 1st.
Go to a scintillation layer where they give off visible light, those visible light photons collected by detector. So there's an intermediate conversion step.
Whereas photon counting is direct conversion of X. Ray.
Um, to electrical signal, so, with these dose reduction technologies, being available.
Um, they're in addition to that, and to supplement that there have been activities on the professional society side. So, some of you may have heard of, of.
A program called image gently, um, that was created initially by the Society for pediatric radiology, the American college radiology, American Association of physics and medicine and American Society for radiological technologist.
Those 4 started this movement now there's over a 100 organizations that participate in the.
And its focus was on reducing those to pediatric patients and they've, the wording they use now is right sizing the dose.
So that we can get diagnostic imaging.
Studies from them, but under using much less dose than we would have used some years ago.
There's also an image wisely movement started by analogous organizations. Maybe not the society pediatric radiology, but a lot of groups are now involved in that. This.
For a similar initiative, but but, uh, aimed at adults.
The focus on dose reduction and.
Has been in office at a point of emphasis in both of these activities.
And it's not just a deployment of these technologies, but also getting radiologists comfortable with reading images that.
Are still diagnostic, but it may not have the may have a little more noise, or may not have quite the same image quality that we're used to before this.
So what's happened? Do radiation doses are typical exams have been reduced.
The radiologist, I would say are reasonably happy with the image quality. The images are diagnostic.
Of course, they missed the old days when the images were just exquisite, but that was that that that used to the noise was down but that was for people referred to as high dose studies.
So, they're reasonably happy with image quality.
But the point that I wanted to bring up today was.
Describe the effects on both quantitative imaging.
And the effects on machine learning algorithms.
And let me try to walk you through some of the concepts here.
1st, we'll start with a with a statement.
That's reasonably true. Which is that is inherently quantitative.
Uh, isn't it, I mean, after all each box or reports the number.
That H, um, has a, has a number that has a unit associated with housing units and those units are defined internationally. And here's a formula. I promise that's the only formula I'll share today.
But it's really well defined so the water.
And this is the symbol for the linear attenuation coefficient. So when the linear tenuation coefficient of water.
Is inserted into this equation you get 0 house for units.
And when you have air in there, which means.
Uh, this view would be 0, you get -1000poundsfootunits and this scale is recognized, um, around the world, it's internationally recognized.
So is inherently quantitative, right?
So, um, so what should be the issue? So we started with a simple experiment. This was work that we actually did when we were participating in the quantitative imaging network.
We scanned a homogeneous water Phantom, so it's, it's water so the value should be 0 housing units.
And we repeated a few times with reducing the dose and then we varied other parameters, such as reconstruction kernel and the algorithm.
We wanted to see what happened, and then we wanted to progress from that to a more complex experiment where we did this with patient datasets.
Not scanning the patient multiple times, but.
Using techniques that other people had developed to simulate the dose reduction so we would take the 100%.
Dose whatever we were doing clinically and then add.
Put on noise into the projection data to simulate the dose reduction of 50%.
All the way down to 25% all the way down actually, but 3 of what the original scan was done. So, let me let me walk you through.
Uh, what we observed, so this is a series of.
Square regions of interest of a water Phantom.
The the top row I'm sorry, the labeling didn't come out. Well, but that's our 100% dose.
And all these are 17 military scans.
They are they have the same slicing.
But different reconstruction, colonel, so this is a B10 of smooth colonel and medium colonel b45, which is a reference.
Uh, kernel for, like, a chest exam and which is a little sharper.
You can't tell the difference in resolution, but I think you can tell the difference in the variability.
In this image, and this is all water there's this so it's supposed to be it's.
It's a uniform object there's no question about that.
And, um, as opposed to patient data where you're never quite sure what's underlying.
Here we were constructed with a innovative reconstruction algorithm.
And here's 1, that's a little sharper. It's still a.
A little sharper, it's still intuitive and a sharper algorithm that we have.
On the Internet side, so the red lines.
Uh, are a map of the distribution of the boxes.
In terms of their house for units. So, for example, in this figure, this red line indicates.
That the distribution is very narrow, it's almost completely homogeneous and centered right around. 0. you can't tell that as a, but it is centered around 0 as opposed to.
See, how the distribution widens as we get to sharper and sharper curls and we get a wider and wider distribution here.
All centered around 0 As we reduce the dose. So we took about 45. I don't know.
Reconstructions, and you can see from here and I'll draw these out in the next slide.
How uniform this images versus how.
The distribution how wide the distribution is here at lower dose in a sharper kernel.
And even here where it's really almost.
It's fairly any peak at all. The mean value is still centered around 0.
But I think you can appreciate the standard deviations of each of these.
Are going to be very different, depending on the dose level, and the reconstruction kernel that we use.
So, if I show you this image.
I pulled out a couple of them just to show the variability.
These are all the same object. Okay. They're all water. They're just imaged under different.
Conditions and we construct it under different conditions. So the distribution is quite substantially different.
As you can see here, by the way, the main value doesn't change.
But the standard deviation does so, what does that mean? It depends on which features you're looking at and depends on the task you're looking at and things like that.
And I put their so what happens to texture? Uh, that's a good question.
Also, I wanted to add because I might have a slide coming up.
If you just looked at the main value, you would see these would all be the same.
However, if you applied a threshold and said, I'm interested in looking at all the boxes that are below a certain threshold.
You could see the difference here would be quite different. Um, even though the threshold would be the same.
It would be quite different, depending on the dose level and the reconstruction kernel.
So the number of boxes below that threshold will change.
Pretty dramatically because the distribution of options has changed.
Even though the main value is the same.
If we take this and apply this to patient data. So here's a module that we had collected clinically.
And we reconstruct it a couple of different ways and we took that raw projection data and simulated.
Adult reduction to 25% of the original, those 10% of the original dose and 3% we went down to 3% because that's the.
Theoretical minimum that the scanner could, um, acquire, uh, an image with they have literally the minimum I may.
And I think if you again look at the differences, and again, the red is showing the distributions.
If you look at how these appearances of the modules.
Barry just due to these imaging conditions, so.
Um, as you can see, as you go down and dose, the noise will increase.
And as you go across and go to different albums, the nature of the noise will change.
So, let me show you what that looks like when you put them next to each other.
Okay, again, here's 100% looks very, very smooth. That's the smoothest 1. um, well, this is maybe a little smoother and then I dropped down. Sorry that's down to 10% dose.
But a smoother kernel and then I.
Used to filter back projection with 3% dose just to say yeah again, the main value is about the same, but you can see the distribution changing and the texture I think you would agree is going to be quite different from,
from each 1 of these different images. images
And now without changing those.
Uh, I'm going to show some, the effects of some other parameters just to show that dose is not the only determined in here.
But I'm going to keep the image on the left the same and you can see this module.
Down here and I'm going to change.
1st, the slide signal. So if you compare the image on the left in 1 millimeter thing.
Versus a 3 millimeter thick image, you can see that. This has a little more volume average. It looks a little more smooth.
And I make it to a 5, you can see the difference in sharpness between these 2.
Even though the reconstruction kernel is the same. The image thickness is a little is different.
From 1 millimeter to 5 millimeter. Now I come back and I'd go back to the 1 millimeter, but now I use a smooth colonel.
Right. And now it's just like you're up the optometrist right here, which do you prefer a, or B, and you can see most of us, I think would say, well, the 1 on the left looks much shorter the 1 on the right.
cerumen blurrier now I sharpen it up.
And up to the algorithm, and you can see 2 things 1, the lines look much sharper, but there's a lot more background noise that's being enhanced here.
Okay, and even if you look at the soft tissues down here in the posterior the patient, and see quite a big difference here and radiologist often look at.
Reference structures, such as the subcutaneous fat or.
Um, locations within the heart, and maybe, um, some muscle and use that as sort of a reference to gauge.
Uh, how much noise they can expect in the image so here's the narrative reconstruction.
i26th with a lot of smoking going on.
And by 44 string 3, so somewhere in the middle here.
And then sharper again with so, even with intuitive, you have the ability to go smooth and sharp and this has 3 that Siemens uses allows a degree of strength.
So, how much blending you do with your original filtered back projection so we get this wide range of parameters and.
And a wide range of appearances, and I want you to.
Start thinking about how that is going to affect the quantitative imaging and any machine learning or algorithm.
And here just a few more images to keep driving this point home.
This is the same area I think I messed up on this last row but so I hope you'll, you'll get the, it's not quite the same location, but here we took.
Um, data from a lung cancer screening case and simulated the original was 100 dose.
We simulated 50% of those reduction down to 25% of the original.
10% in the original, and then we do that for a smooth reconstruction, medium, reconstruction, and a sharp reconstruction and you can see here.
Sorry in each row, there's a point 6 or 1Million a 2 millimeter thick slice.
So, in this very thin.
Point 6 millimeter at very low dose and a very sharp colonel.
I could see, the noise is sort of overwhelming there compared to the anatomy that you would see.
Here, which is about our reference condition here, which is 100% dose 1 millimeter.
And a medium crew, if we apply some noise into that.
I'm going to toggle back and forth between this W, is weighted filter filter back projection. That's a non iterative reconstruction sort of a conventional. If you will apply some D noise into that.
So, pick out a, uh, Square and.
I'll toggle back and forth and you'll see how the noisy let's see.
Let's say right here at 10% dose at point 6 millimeter and medium colonel.
And if I toggle that noise, you can see how the the noise has gone down. We've also got a little more blurring in here.
So, that doesn't come at a at 0 cost.
The shortlist has gone down because of the learning that you get.
With Siemens with their, this is their 2nd generation of innovative reconstruction. Now they have a 3rd generation of admire.
But you can start to see at the low doses, the 10% doses. They really start to fall apart.
And it becomes basically uninterruptible, even if we.
And and here's a comparison. Whoops. Sorry.
Here's back to the filtered back projection.
Is just as a comparison with the.
And then if we do noise that you can see, we get some of that back.
And some of the really low doses, but some of it, we don't, we just don't.
Um, so at at some of these intermediate conditions, it actually goes pretty well. So Here's sapphire plus the noisy and then Here's back to the original images. Okay. So, 1 more time.
So far plus the noise and we can smooth. That is here.
And then the WFP, what we did in 1 of our studies.
As we applied this to, um.
Emphysema scoring where we were looking at again, the threshold that I was talking about before. So we're looking at fox's that are below -950.
And we were doing this with different kernels. Uh, the blue is the smooth colonel. The green is the medium current. All the red is a sharp colonel. Here's a slide. 66.6.
The 1 and the 2, and then the dose levels, and you can see that as you reduce the dose.
You start to get substantial differences from the original value of the original reference from, you.
If you do apply somebody noisy, you can get.
Some, um.
Reclaiming of that you can restore this so we get the correct scores. This is from Y axis is the difference from the original.
Assessed value of of mitus, 950 assessment. You could see it the really low dose as we start to diverge a little bit, but even at 25%.
We're still getting good. Um, um.
Good matching with the original values there um, except it really thin slices again once you really start to drive the.
Noise conditions, thin slices and lower doses that becomes very problematic and trying to restore it with bilateral filter.
Okay, so, just to summarize where we are so far.
The lower radiation dose is.
Comes with an increase in dimension noise, the automatic exposure control that manufacturers.
Have, um, installed does adjust to patient size and anatomy so that.
Helps us control, uh, that image noise to a certain extent.
More efficient detectors helps us to use lower doses without incurring a lot of image noise and advanced image reconstruction.
It does reduce noise, but as we saw its goal is to maintain resolution and limit this moving. But there is some smoothing that does.
That does go on some folks, including us have tried some adaptive, too noisy where you smooth only the homogeneous areas.
And that works to a certain extent, but it leads to some in homogeneous.
Okay, so let's talk for a few minutes about what the impacts are.
On quantitative imaging and machine learning and a.
And I'm going to submit to you that this depends on the task that you're trying to accomplish.
Whatever it is, that you're trying to measure what property you're trying to measure, and there can also be patients or disease dependencies and I'll try to give a couple of examples of these. So, so, for example, reducing the dose does increase the image noise.
And the reconstruction may affect the resolution of the smoothing.
But as we saw it in the water phantom may, even in the module, the main value.
Of a small region may be preserved. So if the task is centered around the main house, we needed a region then.
You can incur all of that, increase the noise and still perform reasonably well.
So some tasks may be robust with respect to the increase in noise for example.
In this solid module, even though we changed the appearance dramatically.
Uh, the detection of that solid lung nodules, especially if it's greater than certain side, like 5 millimeters like this 1, we're unlikely to miss it. Even though this change in properties.
So, we'll natural detection missed 1 of these. It's been our experience that these things are pretty robust.
Certainly down to 25% of us in some conditions, even down to 10% 3% things do start to break apart, but it can be reasonably robust.
To the increase in noise. How about the characterization of a lung module? Well, that seems to me like that's going to be affected by the dose reduction, unless you're just looking at the main value because you can see the change in distribution there.
So, I would think the texture based.
Future values, standard deviation other kinds of values are going to be highly affected by things like to do send a reconstruction colonel.
So, again, it depends on your task. How about the assessment of treatment response? Are you going to be able to.
Detect the change and leaves us leaves and size again pretty robust.
Down to a substantial dose reduction, um, you might see at very, very low doses where this breaks apart and segmentations are gonna be extremely difficult but.
To a pretty far amount of dose reduction. I think the legion size will be very effective, but people also want to use other parameters.
To look at the effective treatment response and radionics, for example, has been used.
To predict treatment response for non small cell, lung cancers and and, and with some promise, but these are going to be susceptible to changes in technical parameters.
So some of the work that that we have done in this area.
Crosstalk stuff a little young. We saw a little difference.
In leaking volumes across those levels, maybe 5 or 6%.
And a little difference across reconstructions when the task was assessing lesion volume.
Uh, 1 of the conclusions was, the reader variability seemed to be affected more by natural size and complexity.
Then by the technical parameters, and I think, I'm sure radiologists would agree with that, that the knowledge of complexity and its composition.
Among 2 things, so those are more related to the region itself than the technical parameters. And there's probably some interaction between those.
Uh, but so it's not just the dose reduction here.
How about radio make feature calculations?
If we were trying to define a region and do radio feature calculations on each of these 36.
Conditions well, this is what our former students no strong nominee John did in her paper that came out in 2 years ago.
So, across all those 36 conditions that we just looked at.
She looked at a number of different radio features.
And looked at the concurrence. Oh, concurrence correlation. Coefficient.
And with the set, a threshold to point, 9 is saying good agreement across all those 36 conditions. And we didn't have a single feature that reached it across those. Now, to be honest.
I'm not sure that's fair because we really put some really bad conditions out there, but.
We just didn't see the reading on the featured concordance.
And she generated a heat map where we broke it down by.
Different subsets where we look at just the dose effects.
And then we broke it down by Colonel effects, and then by slice sickness effects again, green color indicates high concordance.
Correlation coefficient and the red shows very low and you can see that.
And these are the features broken down by groups along the left hand side here and you can see there's just not much agreement. Once you start moving.
Through these parameter conditions.
Okay, so again, the affects depend on a lot on the task.
What you're measuring and again, there are also some patient slash disease dependencies.
So, let me not leave you on a on a completely negative note here, but.
Um, some of the potential solutions are to 1st investigate and then understand the sensitive of sensitivity of these tasks to variations, especially in the imaging protocol.
So 1 solution is constraining the protocol parameters.
And not just, uh, look at your, but.
To be very specific in the parameters that you constrain your dataset.
Um, to be from, it's not easy to do, and it's really difficult to do with retrospective data.
And I give this as an example, we did this this is the protocol.
Chart that we developed for the national and screening trials.
Oh, my gosh a long time ago it was 20 years ago now.
Where we had for each scanner, a set of parameters, and we limited this and we told them what.
Slight sickness what dose level what colonel was really, really painful to specify that and it took a lot of control and it's probably not a very sustainable effort, but that's 1 potential.
And as a business, I feel obligated to mention that you can always come back and constrain the technical parameters hard to do in retrospective data sets.
Um, for machine learning in a I.
Defining the task, but having some understanding of the acceptable range of CD protocol parameters, maybe not all 36 of those conditions that I threw out. There are reasonable. Some of them I did specifically to push beyond what we normally do for clinical practice.
So, we make sure we find the limits. limits
But if we can define an acceptable range of CD protocol parameters, then train and test to those parameters.
Um, if the system is trained on the data, I don't think we should be surprised when the system performance decreases when it's used on cancer screening data, which is done at a much lower dose. There are some screening cases in the but.
80 to 90% of it was not screening data. Uh, similarly, if the data, if the system is trained on data from 1 institution, I don't think we should be surprised when the performance decreases.
We applied to data from other institutions. We've started to encounter that generalizability problem.
So, a couple of other potential solutions, how about normalization can image data being normalized to reference standards. So, for example, can smooth scans be sharpened and vice versa convince license being made to look thick.
And vice versa, uh, can hide those scans. We made to look like low dose and vice versa.
Uh, it turns out some of these are easy to do. Some of them are more difficult to go from sharp to smooth.
So, difficult to go from thin to thick and from higher dose to go to low but it's really hard to go from low to high.
Uh, how about quantitative features? Oh, sorry. I guess I should say.
That doesn't mean, we're not looking into this. It doesn't mean we're just trying to see what things are available there and what options we might be able to present to the community.
So this is being pursued, but normalization also can take place at the quantitative feature level.
So, to see if the quantitative features themselves can be normalized to reference standards.
So techniques such as combat, which treats protocol variations as a batch effect. So you estimate these effects based on certain parameters settings, and then make adjustments in the feature space.
Um, other techniques that investigate the relationship between the imaging parameters and feature values.
Such as going all the way back and looking at.
Special grade level, dependencies, matrices, dependent on resolution and noise is another approach, but it's a sort of an end to end level. So we can look at the effects of.
Slicing is colonel on revolution and noise and then keep going and.
And follow the linear process of special grade level dependency matrices, and look at those effects and look at the sensitivity there.
No.
With regard to to, uh, deep learning.
Features I thought I'd put this up there, but just kind of shake my head. Can these features be normalized to represented? I.
There's a lot of nonlinear analysis that that are needed here and lots of questions. And so that's about all I can contribute to that discussion but it certainly things we're thinking about, which is.
Haven't I haven't heard much about, um, uniformed approaches there.
Okay, so in conclusion.
Um, the technology continues to advance to get better image quality at lower dose.
This is a really good news for our patients and the practice of medicine frankly.
Uh, but it does propose challenges for quantitative imaging machine learning algorithms.
Um, the CD dose reduction does introduce variability in image data.
And the variability is dependent on the task and the parameters used.
Um, but the good news is, there are some approaches that we're looking into to mitigate those effects.
Such as diverse, or at least appropriate training datasets, the normalization of image, data and feature data.
And I think with that, I'll stop and see if there's questions that I really appreciate the invitation and opportunity to come.
Talk with you. All excellent. Thank you. Michael. Do we have questions from the group?
Can I look in the chat let me let me let me start off by asking you. 1, Mike.
Am I correct then and what I hear you say that a reduction in those is reducing the number of quantitative imaging tools that are available for,
for accurate use.
Well, that's a stronger statement that I was going to make, but, but I would say.
Again, it depends on the task because some, some task will be robust.
To the increase in noise, but I would say.
For other for some applications, it's going to be a challenge. Yes.
How's that that? Roughly enough answer.
Yes, no, that's what I thought. I heard it's it's a it's a challenge, right? Yeah, you're right.
Any questions from the group is good.
Literally, um, and please, you can also unmute yourself and just, uh, ask your question or make make your comments. So, Jeff, this is Jeff.
Good to see ya. You sort of could you talk about, um, you know, the, um.
The iterative Recon for GE versus and the kernels for versus Siemens you and I worked a long time.
With coronary calcium trying to get those dialed in. Yeah when you talk a little bit about the.
You know what your experience is between the different vendors and the implementation of the same concepts.
That's a great question, Jeff and and.
The coordinator calcium is, uh, maybe we shouldn't be talking about the foot of, but it's a, it's a good application because.
It's it's a specific task, right? And that gave us a basis for trying to evaluate, but that's a struggle. So, let me let me give a better answer to your to your question.
There have been attempts, but there, I wouldn't say any of them are widely.
Adopted or accepted in the ability to compare.
Kernels and approaches between vendors I think they would all say yep, we do iterative. Yep we do dose reduction.
But how to compare them directly, it's extremely challenging, extremely challenging because.
What normally what we would think about doing is developing a phantom and I see there's a question about.
Qa prior to radio mix and.
There's there are there's 1 or 2 radio exchanges out there.
And people have scanned it under different conditions, but.
You haven't exactly found a way to map from 1 to another. So if you're looking at a.
You know, um, Internet approach with a body.
Colonel or, let's say standard, because that's an easy 1 then then you have a menu to choose from from Siemens and which 1 matches. Well.
You could match it on the noise, but not the resolution, or you could match on the resolution, but not the noise. So, it's a, it's a really challenging problem. And I.
I wish I could say, I have a better a better solution that I could pull out of my pocket and say yep, this is what we do, Jeff, but it's and frankly, there's not hasn't been a lot of incentive.
To get the manufacturers to try to do something.
Standardized in the way that we think about it for quantitative imaging.
I'll pause and see if that is at least somewhat answering to your question.
Oh, yeah, I I mean, I didn't expect you to have an answer. I yeah, yeah.
You know, we think about these and teaching our residents and faculty about them. A number of permutations on a given protocol are so high right now. Yeah.
And then the demands on the clinical work flow for patients to be seen where they want to be seen often necessitates different scanners, different brands. And.
I only see the number of permutations going up knocked down. Yeah.
I, I can't disagree with you, especially with the proliferation of.
You know, we are now in 3rd generation of intuitive and 4th.
Generation, if you, if you count deep learning as a as a method, so how would you compare that to a scanner that.
If they got scanned on, it didn't have any of those. So we just had the 1st generation of intuitive. Um, these are substantial challenges and.
And I I think that it could be approached.
From a test point of view, but and I think that seems like a, um, a, um.
A radionics phantom might be a way to go, but I'm not sure that there's, you know, there hasn't been substantial agreement or.
Or or uniformity across that. So.
We'll put that as a future work. Sorry? Yep.
Mike, do you see a role for.
Societies or the regulators in.
Achieving more progress in this area you think that the FDA would be able to.
I don't know push a little bit more on.
Interchangeability, uh.
Yeah, that's a really good question Paula and I think that was pulled up right?
Yeah, so it sounded like you. Okay so, um.
But, ironically, FDA was 1 of the 1st groups in on Cuba, right? And they did phantoms and but I, I find it hard to.
To imagine that FDA is going to say.
You know, you're going to have to label algorithms, or you're gonna have to here's a standard that you're going to have to meet that just doesn't seem within their their preview.
Um, yeah, I think that many of us thought that maybe Cuba would play a role here and.
Probably somebody's going to be getting together and.
Wishing I think it would take something like that, because I think what I hear when we talk to vendors.
I think they'll say, well, you know, you're interested, but, you know, it, it's not like the 1st thing on.
The list of people when they demand are, you know, and features in our product.
Um, so I think it would take something. Yeah, like, for example, the image group is there a.
Image quantitatively or image intelligently or something. I dunno.
So, um.
So, I, it would it would take something like that to really drive. I don't the vendors aren't, aren't.
Actively opposing these kind of efforts, but but they're waiting for somebody else to drive them.
So, thank you as a representative of Cuba, this is Sean feign, you know, our efforts to try to standardize lung density. Yeah, we're challenging in the sense that the, you know, the goalposts keep moving.
Right? So.
You can be very conservative about your protocol to make it quantitative for the current.
Landscape, but then, you know, new technologies come in, like, for example, or now deep learning, all of those are new parameter sets that you have to work with, to standardize your acquisition so that you get consistent measurements. So.
I think it's just going to be, unless, unless there's and I don't see it. I agree that there's not going to be some degree about this.
It's gonna take a concerted effort on the part of the community to continue to standardize our approach so that we can make these measurements more quantitative and you're absolutely right. It's going to be task dependent. And, um.
We need to focus on, you know, working together to standardize our acquisitions basically to ensure we get the right numbers.
On 100% agree and in fact, I want to emphasize 1 point.
This goes back to what Jeff was asking, and even the, the colleague who asked about QC efforts.
Some of these algorithms are adaptive and so what does it mean that means that.
Um, it would respond differently.
Patient to patient, so the amount of enhancement or smoothing.
It depends on can can depend on the local environment.
And by local environment means anatomy pathology, patient size. So it makes these things really difficult to get consistent because they're not linear.
Um, and I hate to sound like a Luddite, but, you know, at least with filtered back restrictions, that was a reasonably linear.
Approach to it, so it was predictable, but now.
Even if we have a a phantom.
We can't guarantee that the patient, uh, that they're an algorithm will behave the same in the patient as we would in the Phantom. So I, it's just extremely challenging and.
And it's going to take some substantial effort and, and probably defining bounds of what's good enough because we're probably never going to get it quite 100.
Yeah,
agreed sure there is a comment from
in the chat. Um.
Stating, uh, is a huge opportunity, and she agrees with all the challenges, uh, outlined and she also agrees with Jeff.
And, uh, Jeff has another question.
Could we develop a Rosetta Stone project in.
I'll let somebody else answer that I can tell you that, um.
We, we have John boon, and I have approached the manufacturers once many years ago and.
And he was chair of the subcommittee, and maybe we could do it again, is to get the manufacturers in a room and lock the doors and see if we can get a Rosetta stone or something to agree. They agreed in principle.
But we just couldn't get them all the way there. So.
And by the way, thanks, Sheila, for the call, Mike, it stays corny. Um, so the finding that.
Messing with the parameters and the noise and sickness and so it doesn't really affect a lot of things. It's kind of surprising in light of the body of literature related to the old concern about Lucy image compression.
Before diagnosis, particularly the quantitative applications and so, for example, the cardiologist did lots of studies on a lossy compression of.
X Ray angiography, coronary artery and diagrams and found that it did make a difference, it affected their threshold decision making as to whether or not to go ahead and stand. And so they kind of forbade WLC compression beyond a certain amount and so on.
So so how do you reconcile that with this sort of modern finding that doesn't affect radionics features? It doesn't affect certain kinds of, um.
Machine learning, is it because those tools are are not yet using the maximum amount of information that's actually in the images because of down sampling normalization, whatever.
Okay, 1st, let me let me make sure we're on the same page, David. Um, I think I was trying to say there are some.
Elements of some tasks that are not affected by these parameters.
And I would, I was trying to be a little bit unclear, but maybe I, maybe I've blurred it myself.
The main value in a homogeneous region.
Has been pretty robust to changes in dose and kernel and thickness.
But that doesn't mean individual pixel values.
Uh, don't change, so they, they do, and we saw the change in distribution. So, even though the mean of a region was still maintain the same mean value, the distribution would change.
And that meant, for example, I tried to get was.
If you're looking at a threshold, so you're looking at the tail of the distribution that.
The percent contained in the tail of the distribution changes a lot. If you pick us a specific council threshold.
So, I think it is sensitive to some of those parameters and the, the radio mix.
Um, slide that I showed, I think showed only a couple of features that were.
That were robust to to, um.
2 changes in dosing, significant kernel. I think the vast majority of features are not robust. So I, I hope I didn't get the message out there that.
Lots of things are immune to this. I, there's a couple, but I would say the vast majority still need a lot of effort to normalize or or make reference to.
Oh, that's good. Yeah. Somehow I had got this sort of subjective impression from your entire tool, but we were worrying about this too much and it didn't really matter, which is not the message you're trying to convey after all. Sorry? No, it was I know alarmist. Gee. I thought it was going over too far.
No, I was hoping it'd be extremely alarming, but anyway, thanks. Thanks. Bye. Thanks. And I apologize if that wasn't clear.
I was trying to be a little bit hopeful cause I'm really interested in these normalization techniques.
Both at the, at the imaging level, and at the feature level and I think there are some promising things are in some really.
Intriguing approaches that I think are coming, but.
You know, whether they can overcome all these, these issues that I will have to see and and I will be very, as we've talked about a couple times very task dependent.
Um, okay, so there's 1 question. Oh, good.
So, what challenges and quantity of energy to be addressed to some extent with the advent of photo county city.
Since it would along monochromatic image generation or really good question. Really good question can't wait to get our hands on 1.
Um, and the physics of those systems are really interesting because.
The way they acquire the data, and the way they can.
Be Binned and we configured into those monochromatic.
Equivalent beams allowed for a really interesting physical.
Evaluation because we know what the attenuation characteristics of iodine and calcium are, and we should be able to see very clearly if the monochromatic.
Uh, images follow what their our expectations are and if that's the case, then certainly, I, what I'm hearing.
Is there is some real interest in promise in quantitative imaging.
Um, approaches with the photon County, but it's really early days. I mean, this system's only been commercial for a year and a half, even though prototypes have been out there for 4 or 5 years.
I think the latest prototypes probably only been out there about 3 or 4 years.
Um, so I, I, that's a really good question, and I think that's another reason to be optimistic. So.
Watch this space, I think, in the next year or so hopefully we'll hear a lot more about that.
Other questions.
Not, uh, Michael, thank you so much for your time. Your experience.
I can turn it over to you now, thank you.
Thank you, uh, I would like to introduce my colleague, uh, Dr,
he has joined our group at the Frederick National laboratory and he's going to tell you about an RFP that we just put out on a street.
Then then, please, go ahead. Thank you. Um, we have our project called the Amy annotations.
Amy is, uh, I, in the medical imaging.
And the goal for the project is trying to pro creating.
Um, high quality annotations for.
Cancel images from the the image could be long as a black cancer or prostate cancer.
So, if you have no questions, or if you are interested in the project, I can reach out the job.
Under this, uh, contact information.
And I'm going to send you the link in the chat box, so I can check the information.
And also there are some important days.
For example, the February 2nd, uh, seconds.
The deadline for request the RFP publication the 1st is ask some questions.
And the market has to submit the proposal and the.
Probably in the middle of the April to announce, or what uh, our date.
So, if you have any questions, or if you are interested about the project, please connect with the job.
That information, or ask ask for the.
Thank you, uh, back to you, thank you.
I am going to give a brief update on the imaging data comments as we do on every of our webinars. Uh, I want to focus on outreach activities this time. Um.
I think la at the end of last week, um.
The imaging data comments was featured in a Google block post, and after I'm over the slide, I will, uh, post the link in the chat for you to see and that's, uh, quite an accomplishment to being made,
uh, the topic of 1 of those official Google. A blog posts, so please go ahead. Please take a look at it. And, uh, yeah, let us know what you think. I also want to mention that.
The imaging data Commons had, uh.
Workshop session at the project week that was just, uh, conducted last week.
And after introducing the imaging data comments,
the team focused mostly on use cases and the team focused on how ai models can be.
Um, can be used to create annotations for existing, uh, data collections in the imaging data comments and how to do this on large, large scale.
And Here's a link to the document, which I will also post in the chat. And it has it is linked to the slide presentations that were shown doing this project week.
Uh, and we have also 1 of the presenters on the call today, uh, from the break and women's team. So if you have any questions, just send him a chat message.
And if not, please take the time to look at these 2 documents to Google blog post. And the document about the project week.
And I'll hand it over to Justin or John to give the update.
Excellent. So, um, we have a couple of new data sets to announce this month.
Um, the 1st, 1 is this, uh, cervical cancer tumor, heterogeneity dataset. Um, this is looking at serial functional and molecular imaging across the radiation therapy course, and advanced cervical cancer. Um, in this dataset.
In addition to the imaging data also includes tumor volumes and contours for each.
Time point.
Uh, and then we also released another 1 of the annotation datasets from the, uh, the funded activity that we've got going with the NCI, clinical trial network data sets.
Um, so this 1 is annotations for a combination chemotherapy with or without radiation therapy and treating young patients. With newly diagnosed stage 2 to 3 or 4, uh, one's tumor.
Um, and so these are segmentations.
And see points, uh, and also, um.
Letting, you know, when there was negative findings on a given scan so it's a really excellent, uh, addition of curated information about the scans in this trial.
Um, we also wanted to announce a partnership that's been ongoing for a while. Um, but has now resulted in a.
A bio catalog, uh, or image catalog, um, type of, uh, resource. Um, so this, this is, uh, the University of Arkansas team that is, um.
Doing a lot of the hosting and management of the cancer imaging archive is also participating in this image initiative.
Um, and this, uh, you can image catalog that just came out recently, allows searching across different data sets as well as other data sets generated through the UK image initiative.
Um, and then just a couple of, uh.
Technical updates we recently.
I've been investing a fair bit of effort and putting together Jupiter, notebooks and Python packages to help make it easier to get data out of, um, and to perform a lot of other, uh, you know,
common tasks that you might be trying in terms of queries. And things with the APIs. Um, so there's a, a Jupiter notebook that we just came out with, um, that specifically addresses, uh, accessing pathology data.
Um, so something you may know that, um, we rely on this. Ibm is spirit tool.
That, uh, allows spoke download of the pathology data sets due to their size.
But the normal way of accessing this is by installing a browser plug in and so for folks that are trying to access pathology data sets from the command line. Um, there wasn't a way to really do that.
Um, this notebook explains the steps necessary to be able to install the prerequisite software and then how to retrieve the necessary link, um, from to then, um, run a command line, uh,
as part of a command line. Uh.
Steps so that it can be done without having to go through your browser. Uh, and then the other thing is this a utility, uh, Python package that's been released.
Um, and so now you can, if you're working in a Jupiter environment, um, or Python environment, you can just Pip install utilities.
Um, and there's a whole bunch of different notebooks available, um, that walk through different use cases for, uh, you know, doing API queries.
Downloads so a lot of information about how to make use of that package here.
And I think that's it. Thank you Justin. Um.
Yeah, I just support what Justin just said these notebooks are really cool to just go and try them out. Um.
Since we are, uh, at time I just wanted to make you aware that we are going to have another.
Of these webinars in exactly. 4 weeks. And the topic for the March 6 call is going to be chat and potential healthcare healthcare implication.
Uh, George, she is going to present and I invite you all.
To join us then and pop back to you.
Okay, well, my, my thanks also for joining us today, we learned a lot from Michael and look forward to next month. So thank you for your time.
Hello.