When we look at the future of PET, we think of new biomarkers and applications. Is conventional FDG PET maxed out? Or is there room to improve now? No it is not, and yes there is, molecular imaging experts say. While the science will advance as more specific biomarkers come to market, the slow speed of drug development can’t stop the progress of PET. Imaging experts from across the globe believe there is room for PET to become more clinically powerful and more insightful for referring physicians within the current realm of applications. It’s time to take a closer look at how PET is building value, now.
Download a PDF for easy viewing.
For PET to get better, we start with improvements in access. Technologies must be accessible and available to hospitals large and small, academic and rural as well as mobile users. Novel technologies cannot remain restricted to large centers and lofty budgets. Imaging systems and software also need to be strong, smart and reliable, with exams reproducible in all sizes and types of facilities. Technology today must be better and accessible.
And like all good relationships, communication among physicians is key to improving the value of patient care. The written report—or its electronic counterpart—must facilitate effective and efficient communication among reading physicians and referring physicians to offer diagnoses and plan treatment strategies to improve patient outcomes.
“Medical imaging in oncology is progressively shifting from describing the disease from an anatomic point of view to metrics,” says Prof. Frédéric Courbon, MD, PhD, chief of nuclear medicine at Institut Univeristaire du Cancer de Toulouse in France and professor of nuclear medicine. “It is not just about describing what structures are being seen but to convert images in number as a surrogate of either prognostic or an indication of treatment efficacy. Molecular functional imaging seeks deeper insight from a microscopic point of view and have better insight into cell biology to use imaging as a biomarker. It is a personalized treatment. It is very much about quality and accuracy of cancer detection and analysis.”
It comes down to the patient, says Geoffrey Johnson, MD, PhD, consultant in nuclear medicine in thoracic radiology at Mayo Clinic in Rochester, Minn. “When we say there is no cancer we can see, we need the utmost confidence in imaging to support that statement. As technology increases signal and decreases noise, we are increasing the accuracy of those words our patients long to hear, ‘no evidence of disease.’ That is huge.”
In the PET/CT imaging world, technology blends the hardware of PET and CT imaging with sophisticated software. While image quality continues to improve as better iterative reconstruction software is developed, intrinsic troubles remain in that the amount of PET tracer in a patient and the amount of imaging time are limited by practical considerations. This limits the number of detected positron events and makes image noise higher than desired. Because of this limitation, PET images are filtered after reconstruction to make them more interpretable. The goal is to find the image that optimizes some statistic of the acquired data—without contending with the associated image noise.
“With oncology, the emphasis on the PET side has been the detection of uptake,” says Eric Rohren, MD, section chief of PET/CT and professor of nuclear medicine and radiology, MD Anderson Cancer Center in Houston. “Over the years, there have been improvements in scanner technology aimed at improving the resolution and count recovery of PET/CT to see very subtle activity and differentiate that activity from physiologic background. The scanners have definitely gotten better at seeing small lesions and detecting subtle sites of activity.”
Yet, variability of measurements in PET is a limiting factor. “The actual SUVs [standardized uptake values] we measure are not the actual SUV of the lesion,” Rohren notes. “They are the SUV compounded by spatial resolution, partial volume averaging and patient-specific factors we cannot control.”
Over the last decade, PET image reconstruction technology has been designed to provide better image quality, reduced acquisition time and lower injected dose. Current PET iterative reconstruction technologies, such as time of flight (TOF) and ordered-subsets expectation maximization (OSEM), force a compromise between image quality and quantitation. To improve the value of PET