Chip Truwit, MD, FACR, Chief Medical Officer, Diagnostic Imaging, Philips
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.
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, the adoption of dual-energy was slow. Recently, with the latest technological 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—de-noising—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 today 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 being able to reveal “applets” such as the routine reconstruction of, display, and AI assessment of gall bladder images, for example, to ensure the 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, primarily 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 our IQon 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 percent of the time, it eliminates guesswork and may obviate patient callbacks for a follow-up scan to make a confident diagnosis.
What, according to you, are the key aspects that purchasers need to remember while picking up the right vendor?
When I was a department chair, my focus was always on the reliability of the equipment and the partner, but, everyone’s attention is not the same. Some people concentrate on the speed of scanners and some on the scanners’ ability to perform multi-energy scans in a robust and straightforward manner. So, it’s essential 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 the adoption of technology, training, and servicing is crucial to ensure reliability and quality. By this, I mean an integrated 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.