Reviewing The Latest Advances in Medical Imaging Tech with Gabrielle Swasey and Luca Bogoni of Carestream

Episode Overview

Episode Topic: In this episode of Skeleton Crew, host Jen Callahan explores the world of medical imaging with our esteemed guests from Carestream, Gabrielle Swasey, and Luca Bogoni. Together, we embark on a journey through recent innovations in medical imaging, the future of this ever-evolving field, best practices for radiographers, and the crucial role of AI and machine learning.

Lessons You’ll Learn: You’ll gain valuable insights, starting with Carestream’s pioneering technological advancements and their profound influence on medical imaging. You’ll also get a sneak peek into the future of medical imaging, exploring innovations like point-of-care solutions and advancements in general X-ray technology. Additionally, practical advice will be shared for radiographers, helping them maximize the utilization of Carestream devices and navigate common challenges. Lastly, we’ll delve into the transformative role of AI and machine learning in reshaping medical imaging.

About Our Guests: Gabrielle Swasey is an esteemed XRS Clinical Specialist with extensive experience in medical imaging. Alongside Gabrielle is Luca Bogoni, the Head of Advanced Research and Innovation at Carestream, who brings a wealth of expertise in driving innovation within the medical imaging field. Together, they provide valuable insights into medical imaging and its dynamic advancements.

Topics Covered: Embark on a journey of innovation in medical imaging with our esteemed guests. We begin by exploring Carestream’s groundbreaking technology, reshaping medical imaging. Then, we peek into the future, unveiling transformative innovations. Radiographers gain practical insights for optimizing Carestream devices. Lastly, we dive into AI and machine learning’s pivotal role in healthcare. Join us for this enlightening episode as we navigate the domain of medical imaging with Skeleton Crew – The Rad Tech Show.

Our Guest: Gabrielle Swasey and Luca Bogoni from Carestream

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Episode Transcript

Luca Bogoni: We see some poor examples of the ability to identify people in the media. Identification was all trained on Caucasian folks, and automatic identification of African Americans was really poor because the training set was not representative.

Jennifer Callahan: Welcome to the Skeleton Crew. I’m your host, Jen Callahan, a technologist with ten-plus years of experience. In each episode, we will explore the fast-paced, ever-changing, sometimes completely crazy field of radiology. We will speak to technologists from all different modalities about their careers and education. The educators and leaders who are shaping the field today, and the business executives whose innovations are paving the future of radiology. This episode is brought to you by xraytech.org. If you’re considering a career in X-ray, visit xraytech.org to explore schools and to get honest information on repairs, salaries, and degree options.

Hey, everybody. Thanks for being with us today. We are here on another episode of The Skeleton Crew. Today I have two great guests with me, Luca Bogoni and Gabe Swasey. They currently work at the company Carestream, and if you’ve been in the realm of radiology, you most definitely have heard of Carestream. I was just saying to them before we started that I have worked with them since I’ve become a technologist, so I’m super excited to discuss with them the past, the present, and then the future of where Carestream is going in the field of radiology. Thank you both so much for being with me today.

Luca Bogoni: It’s our pleasure. Thank you for having us here.

Gabe Swasey: Yeah, we’re very excited.

Jennifer Callahan: Awesome. So before we get started and delve into Carestream, Luca, and Gabe, could you both give us just a small background of yourselves where you were in the past, and then how you ended up here in Carestream? Gabe, do you want to start?

Gabe Swasey: Sure. So I am certified in radiography, of course, but also quality management. So that brought me around to different things in the field, being able to become a better tech because of it. I’ve been with Carestream for two and a half years now, and I thoroughly enjoy my job as a clinical specialist for Carestream, and I get to visit radiology departments across the nation where meet some amazing people, get to show everyone how our equipment really can make their lives easier, and fell in love with Carestream before I started working for Carestream, actually, we had a unit installed and it’s just so user friendly.

Jennifer Callahan: I’ve worked with, like I said, Carestream in the past and it is very user-friendly. I enjoy the products from there. So go ahead Luca, give us a little background of yourself, please.

Luca Bogoni: Well, I’ve been at Carestream now for two and a half years in the role of Head of Innovations, and my background is long and varied positions in computer vision, robotics, medical imaging, medical devices, clinical study, regulatory and so on. But what I really enjoy doing here at Carestream is leading the innovation, taking ideas into products that have a clinical impact. If they are not going to have a large clinical impact, they lose the purpose. So for me, this is the excitement. At Carestream, I am surrounded by a whole host of folks that are very capable, and here is the opportunity for us to develop these ones.

Jennifer Callahan: So, do you want to give us a little insight to the background of Carestream and how we’ve led into the future, of what we’ll discuss further about the innovations that are going on?
Luca Bogoni: Oh, yes, of course. So Carestream is gone 100 years of experience in the imaging of business, having come from Kodak, Carestream has a long history of game-changing innovations that have become commonplace in the healthcare industry. For instance, Carestream in 2009 disrupted the medical film by introducing this drive view, which is essentially the first tabletop laser printing for mammography in general radiology. So that got rid of all the nasty chemicals that people had to deal with it. In the same year, they introduced this DXR detector which was the first wireless detector. So, you could take detectors and tether them, moving them around wherever they were needed. And in 2012 they introduced the mobile detector, the cart, the driver, and which had also a collapsible column. So, it meant that we could bring the imaging to the bedside of patients. So, a big transformative care, the way to deliver care. And recently in 2019, Carestream introduced differential filtration for dual-energy. What that does it affords optimizing the spectrum while reducing those same image qualities but reduces those for dual-energy. And then just recently, a couple of years ago in 2021, there was the introduction of the first AI-based noise cancellation solutions. So that solution is really interesting because the system is trained to understand an AI system, is trained to understand the characteristic of the noise, and subtracts it from the image. So, you had better image quality at the end, but it also offers the opportunity to have in the dose to the patients.

Jennifer Callahan: So, for someone who might not who might be listening, who isn’t actually working in radiology like myself and Gabe and yourself, could you just do like a brief overview of what noise means in an image?

Luca Bogoni: Yeah, yeah. So, noise is this fuzziness that you get the speckles and it manifests in many different ways, but it’s not crisp and clear. And why is that important? We talk about signal-to-noise ratio, the importance of the information. If you get a lot of noise, it becomes hard to see details that are critical to the diagnostic part, that they may obfuscate the actual manifestation of a disease. So, the ability to reduce noise. Think of having your TV, a high quality, the high definition, the 4K versus the 256 of something that seems far away.

Jennifer Callahan: That’s a great example of it. So, talking about all these different advancements that you’ve done with technology, where are we currently going with it now? Do you guys want to share or what are you currently out there in the field doing?

Luca Bogoni: Gabe maybe can tell us more about the latest technology that impacts radiographers and technologists.

Gabe Swasey: Yeah. More recently we’ve been expanding our offerings of software features. We’re going to help streamline workflow and provide for more consistent positioning. Two of the latest that have come out recently I’m excited to talk about is our smart room and our detector. I travel a lot and I see the same thing over and over in all these radiology departments. Everyone’s short-staffed, everyone’s volumes are increasing, and our solution to that is our smart room. It’s all about helping you optimize workflow and getting you back to focusing on your patient. For example, a chest exam. The equipment’s going to automatically adjust so that the tube in the wall Bucky is going to match the height of your patient. 3D cameras are going to automatically detect patient size and set your technique and your collimation per that patient. In addition, the cameras are going to be able to detect whether or not the patient is in the correct position. And then you’re going to go back to your x-ray console and you’re going to look at that screen, and there’s going to be a camera that you can see your patient on screen.

Gabe Swasey: And maybe they moved a little. You can by voice have them shift or move. But you also have virtual collimators right there on the screen. So, you can adjust your collimation on the fly without having to go back and forth in the room. And so our detector is the second thing I wanted to talk about. So, you normally have those 2 or 3 images. The patient moves and you have to repeat the whole exam. Or you didn’t even realize they moved and now your images won’t stitch properly. So, the detector is a full 17 by 48-inch wireless detector. So, you can get the whole spine in a single shot. And it really stands out for it being a cesium iodide scintillator, it’s going to really tolerate that high and low mass. It’s a faster workflow. You’re going to have less chance for that patient to squirm, and you don’t have to worry about whether or not it’s stitched. It’s good to go. The image quality is really good, but even better, it is a wireless detector. So, you can actually add this to any room with our retrofit option.

Jennifer Callahan: That’s awesome. To this point, starting as a technologist about ten, 11 years ago at point started with separate cassettes that you would slip in and then have to take out, and then you had to put into the processor, and then you had to pick that you had three cassettes, and then it would figure out how it would stitch it all together. And now the one that I’ve worked with most recently, you know, still takes the three separate shots, but it moves the Bucky all on its own. But you still have to have the images stitched together. So the stitching is the tricky part.

Gabe Swasey: For sure.

Jennifer Callahan: Luca, did you have a hand in helping develop any of this or having a little brainpower put into that somewhere?

Luca Bogoni: Yes, I think there is actually some additional exciting technology that comes along with that. It’s the post-processing, the some of the AI that goes into place to make sure that we are able to monitor the patient, whether subtract off to the background, make sure that the proper calibration is done so that we can do automatically determine the technique. So all of these are coming into some of them are already in place and some more of these are coming. It’s important to understand that in the arch of the clinical care of the patient, AI starts at the image formation you can bring to bear the image formation. And then even before you can provide, assist the technologist to make sure that the image is taken right as you remove, as Gabe was telling us, remove some of the variability there before you enter the post-processing, the analytical part, the diagnostic part. And we can talk a little bit more about that. Let me start with that then. And gives you a view of not just AI, but talking about where innovations are coming in which area and innovation that will revolutionize medical imaging altogether. There are three areas. One that continues what we started in 2012 of bringing imaging to the patient, rather than the patient to the imaging. Right. The point of care delivery is the next one. There are advances in clinical and therapeutic. So, there is new role that x-rays can begin to play. And then yet another dimension of this of innovation is called dynamic X-ray image.

Luca Bogoni: Regarding the point of care. So, there are aspects of intelligent and not just I considering all the components that are integrated as part of one system. So not just the detector, the processing, but how they work collectively. The second point of care pertains to interventional procedures that can be performed at the bedside. So these are new directions for point of care. Let’s talk about the point of care and how I can help. For instance, you know that as you are taking images, consistent imaging is really important. If you think of the rounds, the ICU, morning rounds, and so making sure that the image of the patient is acquired through the same angle, the same orientation, with the same technique, because it’s not going to be the same person taking those images. If there aren’t variations that are not due to disease exacerbation, but due to the acquisition aspect, then they may lead to some misinterpretation. And so it’s key that there is an opportunity for AI to be able to recognize how the pose, the acquisition, the angle and all of that, the orientation, the distance. Are they consistent with the previous one? And if COVID has taught us anything about in the last couple of years, it’s about the issue of moving patients and isolating them, and dealing with infection controls. And if you think about intra-hospital transport leads to a lot of patient complications. A recent publication as this past year, 2022, in the American Journal of Emergency Medicine, are reported that patients in the transport from ICU to the imaging modality to a surgical intervention room can experience adverse events.

Luca Bogoni: Up to 72% of the transports that depend on patient condition, of course, and also the skill of the transport staff. Right. But these range from patient discomfort to equipment failure and cardiac arrest. And they often result in lengthening of stay. So being able to deliver that care, that quality, whether it is taking a snapshot reliably or whether it’s taking or offering the capability of doing interventional procedures at bedside is really critical, not just for the patient. But if you think of the entourage of people that have to unpack a patient from the ICU, remove the commode, the ventilators, moving down weight and all of that is an inefficient utilization of resources. So here point of care delivery x-ray. It’s a great opportunity. And with that, I think x-ray is gaining back a larger role both in diagnosis and management. Traditionally x-ray is fantastic for musculoskeletal things. Fractures, chest think of pneumonia, and of course mammography. Although now is digital breast tomosynthesis has replaced conventional mammography. But oftentimes x-rays only play a gateway role. If you think you go to your primary care physician, you’ve had injured your knee, your fall, you had a bicycle accident, and it checks your knee and it says, oh, we really need to take an MRI. But the requirement for the insurance company is for you to get an x-ray that may reveal something or they may not.

It is useful, but it is. It doesn’t have quite the right value. And if you think about MRIs, for instance, or CT, those modalities are really important, but they are not ubiquitous. Two-thirds of the world doesn’t have those resources just to anchor us on something concretely. The US is about 38 MRI per million people. Japan has 57 million people. Mexico has three MRI machines per million people. Wow. The country of Colombia that’s got more than 50 million people as ten. So availability, access to a modality that can offer diagnostic capability, and even patient monitoring. It’s really important. It’s a necessity given the resources, but also given that AI is really transforming X-ray in many ways, it’s become more relevant not only in the context of musculoskeletal. You’ve interviewed in your previous podcast, some folks from Glimmer, right? You’ve talked about fractures and so on, but it’s also expanding in areas that are traditionally land of CT and I want to give you an example, the research that we are doing today that denotes a potential expansion of X-ray as a utility that also democratizes access and is impacting patient care. A couple of years ago, Carestream joined. It’s a consortium, non-profit imaging consortium that in its inception was focused on creating a worldwide repository for patients suffering from idiopathic pulmonary fibrosis. It’s a type of fibrosis, and most people don’t know about it, but there are about 40,000 people in the US who die yearly from that.

Luca Bogoni: It’s 5000 more than prostate cancer. And what’s worse, there is really no effective therapy and IPF is hard to recognize. So it’s not only a lot of casualty, but it’s also hard to find out. So once one gets the first symptom, it takes about two years to be recognized and be sent to a pulmonologist and get a CT. And even then only the survival rate ranges between 3 to 5 years. So it’s a huge health crisis. So wouldn’t it be great if we could leverage X-ray as a ubiquitous image modality to diagnose IPF early, and maybe even monitor with therapies as they become available? So earlier this year, Clearstream and OSI launched a project it’s called Opus that collects all this data we include in x-ray and other spirometry data and other ancillary data, with the goal of including some post-processing to enable the detection of IPF. And we are also partnering with other companies to do this, because any of these tasks, as you probably heard from some of your other guests, can be really daunting. And there is data and so on, and we can talk about that a little bit later. One of the companies, medical IP, is a Korean company that does analysis of X-rays. Really interesting topics there for quantification and we’re doing research also with John Hopkins.

Jennifer Callahan: So is this AI than is it used in obviously it helps in the diagnosis. But could it help with like early diagnosis?

Luca Bogoni: Yes. That’s the whole point. We look to be able to recognize diseases earlier. There is new companies that are doing all sorts of really exciting quantification analysis. I, for instance, do a whole sort of quantification of diseases unit they identified, but it hasn’t been quite touched yet. Also, an exciting piece that is the segway to my next topic, which is x-ray motion. Right. So we are talking about lung compliance. So x ray traditionally is just one snapshot. It’s been that way for hundreds of years. It has better quality and better resolution. And we talked about it. But the dynamic part is where you can see some of these characteristics that are really informative from a diagnostic standpoint. Let’s think about this. Capturing like a little movie mini-movie of an x-ray through a breathing cycle and then applying some fairly sophisticated mathematics and AI to quantify the lungs as they expand, as they comply. If you think of someone who’s got COPD, for instance, the COPD, the standard care is using spirometry. You blow in this little tube, bubbles go up and you quantify how much the lung changes. But you have no idea which lung is complying. Which one is not? It’s therapy effective. Yes, it’s done because that’s what we have. But here x-ray is will offer a fantastic advantage. I think this area of X-ray imaging will disrupt clinical care by just touching the surface. Just imagine what you have done. I’m sure you and Gabe have done many of these musculoskeletal studies right.

Luca Bogoni: You do some floral study to look at articulation. You do swallow studies, right? But what if you can actually instead of taking a standard standing pose on this fantastic one-shot detector that you can see the whole legs, right? What if you can actually have the patient step on something because that’s when he feels pain, not just when he’s standing a step or getting down and or articulating, moving the hand, or moving the arm and say it doesn’t hurt? It really hurts you. Right? So there is information in the dynamics part and there is kinematics. We have done that. If you think of all these movies and avatars and all this crazy stuff that we are doing with media, medical science is bringing medical innovation, is bringing this knowledge from other fields to bear into this area. I think we have there is x-rays at the point of care, static or dynamic, interventional or better consistent imaging. There is the ability to do really for x-ray take a new. Overall, if you think back of it is CT, but CT and MRI are not really available everywhere as the rest of the the world doesn’t run on CT and MRI, right? Portable x-ray field radiologists are ubiquitous and that’s where people need it. And then this dynamic aspect I think we can talk on and on, but I think these are really areas fulcrums that will transform the role that X-ray plays.

Jennifer Callahan: So that was like a crazy amount of information that what Carestream is getting themselves into and a lot of innovations there. Which is highly influential was what will be going on with diagnosing. But let’s switch from the more technical end on there to move into the clinical world where Gabe and I are, and you’re going out as a clinical specialist. Can you say exactly what that means from a Carestream point of view, what a clinical specialist does?

Gabe Swasey: As a clinical specialist? My job is to help sites optimize their workflow with our Carestream equipment, whether that’s going in and making sure that they don’t have any questions, making sure they have a full understanding of what all their equipment can do or like, or just troubleshooting and helping them through things.

Jennifer Callahan: I’m sure that you’ve experienced troubleshooting problems and techs being like, I don’t know what’s going on, like, how can I do this? How can I do that? What are your best tips and advice for a technologist who’s using Carestream?

Gabe Swasey: From Carestream’s perspective? A big part of it is knowing what all your equipment can do. So if you don’t know the features and functions, you’re not going to be able to use it to its optimal capabilities. You mentioned the staff getting new equipment, and we come in for that training, right? Well, sometimes that training is a little overwhelming. It’s a lot. My advice? Ask all the questions while your person is on site. But you can also ask for additional training should you need it. And think we mentioned this earlier too the cesium iodide detector, the scintillators, and how they are really going to tolerate that high and low mass. It might be a good idea to go through your techniques, review your techniques, and make sure your eyes are matching what they should be matching. That way you know what that optimal range is so that you’re supplying the accurate techniques.

Jennifer Callahan: Have you found going out into different sites that some people might be what exactly is that or something that they don’t even pay attention to?

Gabe Swasey: I definitely get a mixed bag on that. Some have definitely fully understood it. They’re living by that number and then maybe not so much. But I think part of that is just a lack of understanding. One of my favorite things is it’s called the deviation index. I don’t know if you’ve heard of that. It actually came out from the IEEE and the ARPA back, I think, in I don’t want to misquote the year, but 2012, 2014 area, they said all these different manufacturers have different numbers and we want a universal number. So the deviation index actually applies to all manufacturers. And essentially zero is your goal. Zero is easy to remember. And then the optimal range is going to be negative three to positive three. Easier number to remember.

Jennifer Callahan: So this new technology that recently came out with you has the 3D cameras. And you’re lining this up and it’s adjusting to the height of the patient. Do you find that you have to maybe do a little bit more explaining or a little bit more like a hands-on approach with sites that you’re going out to help them because it sounds like life-changing in terms of being a technologist, but getting used to line it up or maybe the computer software portion of it?

Gabe Swasey: We’ve definitely gotten really good feedback about it, and a huge part of why, like Carestream is because it’s so user-friendly. That just continues with the smart room, for sure.

Jennifer Callahan: Okay, what has been since you’ve been with the company? What has probably been one of your favorite things about being there at Carestream, or maybe one of the products that you’ve helped implement going out into the field?

Gabe Swasey: My favorite thing is just meeting all of the amazing people I’ve met a lot of people and the connections that I’ve gotten to make, it’s unbeatable. Like, and I wouldn’t get that anywhere else. Maybe a favorite product? I love that that is a game changer in the Or especially.
Jennifer Callahan: So how would you use it in the Or?

Gabe Swasey: Oh, there are so many different ways. So Jackson’s table lowers down. You can stick it under there and do an AP for a missing surgical instrument. You can get that all in one shot and not have to do multiple and try to search and save time when they’re doing multiple levels of an at a site recently and they did 13 levels in one surgery of the spine, you can do one shot and get all of that.
Jennifer Callahan: How about you Luca? Let’s talk about favorites here. What do you think has been one of the favorite things that you’ve worked on?

Luca Bogoni: Yeah, I think there are quite a few favorites in my tenure because as you see, we have a long history here, but I think it’s some of these portions that deal with these smart rooms. So bringing in the smartness in the room in the workflow, how to fit it all together, and then thinking about I’m a little bit ahead of the game here, I’m looking a little bit out. So at the one end, incorporating the feedback that we’re getting from folks like Gabe, that tells us, oh, this little widget could be a little bit better yet. So incorporating that, some of my favorites are things that are still under the hood. So we aren’t we haven’t them out yet. Something that we will see soon is automatically getting just the right pose for being able to provide the standardization of pose for the various studies, to make sure that the source and the detector are just lining up in the right way. If you think about the Odontoid acquisitions, and so you position your patient and yours may be different from Gabe’s, but so the system and as you set them up and you go back to take the acquisition, at that moment, the patient sneezes and then goes back, but they really back.

So the ability to tell you how much the pose has changed from what, and then your ability to communicate without going there, repositioning chin and all that, saying something to the effect. Move a little bit up, open your mouth a little bit, and turn to the right. Not too much to the left. Perfect. So interact with the patient whilst the system in real time monitors the pose. Save so much time. So time. Exposure, consistency. I think those are really exciting components that we deal good clinical value, good efficiency, good satisfaction from customers, from patients, from people that are overburdened by the requirement of taking images and doing all of this and setting everything up manually and the list goes on. But I think more concretely this is one of the features that it’s a little bit there already, but we’re expanding it to all body parts.

Jennifer Callahan: How long would you say that development goes into something before it’s released?

Luca Bogoni: I think things are getting done at a faster pace nowadays because of the components, there is a lot of what they call models out there, models that you can borrow, then transfer learning, and all sorts of things like that. So timing can be a little bit more compressed. It could vary anywhere from two years or longer. Depends. And that is a good segway to that is really to say what goes into making these solutions. Some of them have to do with the challenges. The challenge is getting data, image data that is representative of the population. Similarly, for diseases, you need to have a broader population, geographical as well as representation of disease as well as demographic, because not all diseases are created equal. And in the posing aspect, for instance, there is a whole sort of appearance of people. So making the solution robust quite a bit. This solution that guides and can provide assistance to a technologist is feedback regarding pose where a little bit of error, if I say move, if I give you a sense that it should be moving five millimeters or to the right or to the left, it’s not significant when you’re talking about.

Luca Bogoni: The context of diagnosis, that’s a whole different story. So those are the challenges, the data, the availability of the data. And because there is that comes into place, privacy concern and all of that. But there are solutions for that as well. I just want to give one last teaser, as we are having now this callback, we’re getting together three, or four years from now and where could we be in three, or four years from now? And let’s talk about this example of just the adjustment of the technique of the pose and getting the images looking just right. We have seen in the media the ability to give a description in Midjourney. I would like to see a landscape that is got a star shape and three lot of frolicking bunnies in front of it, and you get a variety of those and then you say, oh, that looks good. So here is the story, there is an analogous where you could describe the type of images because there is customization. Every site has its own little flavor.

Luca Bogoni: Right. There is the stream flavor. But then as you cook your recipe, you add a little bit more salt and you know that your family doesn’t like so much sugar. Don’t know who does, but mine does. It would be good. And what I see is this ability to say, you get that template image or you get your first acquisition, and then you look retrospectively, the system sees through the repository in the packs, and then it knows underneath it how to transform the image to meet your requirements. So that then Gabe could say, or Jen could say, oh, I would like the edges a little bit more sharp in this area, a little bit changing balance. And that’s it. It’s done. I think that’s where we are heading, reducing this reliance on subjectivity, reducing the reliance on experience. Not that is a bad thing by all means, but it affords a leveraging of people that have different expertise, bringing them all to the same level, and assisting everybody equally. So I think these are opportunities that they may sound a little far-fetched now, but they are coming and they will help us be better at what we do, make it easier, and deliver better quality in general.

Jennifer Callahan: You guys have offered so much great information today. I’m so thankful for your time. We’re going to get wrapping up here, but Luca and Gabe, thank you so much for taking the time today to discuss Carestream, the past, the present, and the future, myself as a technologist, I’m looking forward to seeing what is coming out, and I actually hope that I work in a facility that this 3D room, the smart room, is available. It sounds like a dream come true. And everybody, thanks for being with us today. This is Gabe Swasey and Luca Bogoni from Carestream. We’ll see you later. Have a good day.

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