Chip Truwit has 19 years of experience as Chief of Radiology, guiding Hennepin Healthcare’s Department of Radiology through transformation, through its ascent to leadership as the premiere Department of Radiology in the Twin Cities. Taking on new challenges, he is now the Chief Medical Officer of the Diagnostic Imaging Business Group at Philips. As a medical imaging industry veteran, he provides medical leadership and radiology expertise to clinical vision
Please provide our readers with insights about the key areas of innovation and diagnostic imaging technology advances that helped shape today’s medical imaging market.
I was a neuroradiologist for many years, and during that time, I was also privileged to serve as a department chair. So, I have seen things changing. The modern history of radiology consists of four pivotal points of inflection.
First,was the introduction of computed tomography (CT) itself. No one would argue that there have been many key innovations in imaging: MRI, PET, digital x-ray, point-of-care ultrasound, to name a few. That said, it is unquestionably the CT scanner that fundamentally changed radiology and medicine. The CT scanner undeniably is the most important modality in almost any department of radiology: because of CT, lives are saved every day.
Second,was the multi-detector CT scanner, whose speed reached such heights that we could essentially “freeze” heart motion. This opened up a dazzling array of diagnostic possibilities including not only 3D but 4D imaging. Third, the CT vendors brought forth what Hounsfield anticipated, dual energy CT. Despite the promise, however, adoption of dual energy was slow. Recently, with the latest technology advances and innovations in Spectral CT and software, radiologists were empowered to bring this solution to bear on patient care.
Finally, the advent of artificial intelligence is introducing yet another era in medical imaging. The application of AI in imaging is multifaceted, impacting many aspects of the patient journey: workflow enhancements both before and after the patient is seen, during image set-up and acquisition, image reconstruction, both reducing noise (denoising) and dose reduction, and raising the bar on the quality of diagnostic interpretation.
Based on these four key inflection points, what trends do you see in medical imaging to day or on the horizon for radiologists in the future?
One specific CT-related trend we’ll see is better ways to take advantage of image de-noising techniques to both improve image quality and decrease both radiation and contrast dose. Initially, this work included machine learning techniques: iterative reconstruction, model-based iterative reconstruction, both of which were successful, albeit incomplete. These techniques offered considerable advantages in particular, with respect to CT angiography. More recently, de-noising has assumed more of a deep learning character. With successful implementation, there seems to be little question that radiation doses will be dramatically reduced without significant image compromise, which I see as the second coming of CT–improved image quality with reduced dose.
Another trend we’ll see relates to AI and the explosion of data, not just the imaging data, but the entire digital patient from digital pathology and digital genomic information to digital analytics of workflow and performance. One application of AI will focus on analyzing this data to look for patterns across similar populations of patient data. From this use of AI, what will evolve is imaging profiles, genomic profiles, and pathology profiles that, when viewed together, may reveal more insight on patients that could potentially benefit from one type of chemotherapy or that will predict chances of worse outcomes consequent to a particular therapy.
Increasingly, we’ll see that AI will be able to detect more than the average human, by virtue of reviewing thousands, if not millions, of scans as part of the learning. AI will perform these image reviews in seconds, if not milliseconds, and, unlike humans, AI will not fatigue. Thus, for mammography, CT, MR, PET and others, image interpretation will be undertaken by AI software, both as a form of triage (i.e. - Which patients have intracranial hemorrhage? Which have pulmonary nodules, pneumothorax, or pulmonary embolism?), and as a security check against human performance (the night shift tele-radiologist, the resident radiologist, the potentially compromised radiologist or as a screen to identify incidental findings on CT or MR studies).
Finally, a third trend we’ll see is increasing recognition that the addition of Spectral CT imaging data affords more degrees of freedom to the AI picture. Thus, we can expect to see two additional features of the AI story consequent to the recent rapid evolution of Spectral CT. These include Spectral CT being able to reveal “applets” such as routine reconstruction of, display,and AI assessment of gall bladder images, for example, to ensure universal diagnosis of cholesterol gallstones, otherwise invisible on conventional CT. Similarly, AI is likely to render straightforward with one exam – first time right - the diagnosis of bone edema in the assessment of acute versus chronic compression fractures, differentiation of pancreatitis versus pancreatic necrosis, unsuspected myocardial infarction, bowel infarction, and others, largely due to spectral isolation of iodine in CT contrast. In all likelihood, Spectral CT will afford simple diagnoses that were often challenging by conventional CT, many of which typically required follow-up ultrasound or MR imaging.
What are major pain points or challenges that you see when it comes to the medical imaging space, and how is your company working to mitigate those?
The major pain points that are emerging are mainly from inefficiencies in the workflow and the failure to offer patients comprehensive service all the time. Philips is putting a lot of effort in this regard. One key example is ourIQon Elite Spectral CT which can easily integrate with workflow and deliver unparalleled diagnostic quality leading to fast procedures and precision diagnosis. The always-on design, without increased radiation dose, and simple workflow solutions are important innovative distinctions of IQon Spectral CT over other spectral scanners.
The capabilities of a scanner such as IQon Spectral CT make a big difference in an outpatient clinic, for example, where the scans are not immediately interpreted and often, the job of preliminary interpretation is done after the patients leave the imaging center. Because IQon Spectral CT applies spectral technology 100% of the time, it eliminates guesswork and may obviate patient callbacks for a follow up scan to make a confident diagnosis.
In the future, I agree with others that AI will play a vital role in screening interpretation before a patient leaves the imaging center.
What, according to you, are the key aspects that purchasers need to remember while picking up the right vendor?
I’d like to tell you how I used to make decisions when I was a department chair. My focus was always on the reliability of the equipment and the partner. Here it needs to be mentioned that everyone’s focus is not the same. For example, some people concentrate on the speed of scanners and some on the scanners’ ability to perform multi-energy scans in a simple and robust manner. In addition, today, almost every purchaser wants to leverage the power of AI. So, it’s important to choose a vendor that offers an integrated, one-stop-shop approach.
Secondly, building trust is imperative. Customers have to understand that when they buy a piece of imaging equipment there is typically an adjustment period for staff before the system utilization is most efficient. So, having a supplier who is also a partner to help with adoption of the technology, training and servicing is crucial to ensure reliability and quality. In that way, healthcare providers are not just buying a scanner but an integrated solution. By this, I mean a solution that not only meets their needs and financial constraints now, but evolves with their goals or needs over time so upgrades are easier or replacement costs are minimized.
Finally, another key consideration when buying equipment is not just the radiologist’s perspective but the technologist’s valuable insight about the equipment as well. Technologists deal with the patients directly day in and day out and any purchase of imaging equipment should enhance the workflow by connecting all the people, data and technology in the imaging department.
What would be your piece of advice to the budding radiologists on the future of the medical imaging space?
Today, I am seeing that people are becoming skeptical about the advent of AI in the medical imaging space. They have started to believe that radiologists will be out of work. But, this apprehension does not have a strong foundation. Most of the AI programs are capable of answering one particular question at a time, but the seasoned radiologists have the experience and capability to answer multiple questions by just going through the report once. So, AI programs are good for pre-reading or double checking. In addition, AI may play a vital role in the identification of false-positive outcomes—where benign micro-calcifications may appear as malignant.
So, I tell budding radiologists not to be afraid to go into this space because of AI or fear of not having a job. AI has significantly reduced the chances of making an error and will enable us to integrate and assimilate vast quantities of imaging data much easier. Ultimately, that will lead to discoveries that will improve patient care. In that way, AI will be critical to the future of the medical imaging space and the delivery of precision medicine. It’s a very exciting time in imaging right now because we can see so many more things in imaging in dynamic color or 3-D and with greater clarity, and this allows imaging to make a much more impactful and critical contribution to patient care, precision diagnosis and healthcare overall.