Radiology began as a discipline of capturing images, and, like photography, has evolved by emulating and improving on how the eye works. But advancements such as compression and image analysis algorithms and artificial intelligence (AI) are quickly transforming radiology into a discipline that instead mirrors how the brain works.
These new developments may someday render radiology images — as we know them today — irrelevant, according to Daniel K. Sodickson, MD, PhD, who on Monday delivered this year’s New Horizons Lecture, “A New Light: The Birth, and Rebirth, of Imaging.”
“I’m pleased to announce the death of the MR protocol,” said Dr. Sodickson, “Not quite yet, because there is lot of work to do and there will always be need for tailored studies to answer a particular question. But MRI is a little like art photography now: Lie still, don’t move, hold your breath, and do it again. It’s not a very modern paradigm.”
Radiologists, on the other hand, will maintain their value, he added.
“We are more than just our images,” Dr. Sodickson said.
Dr. Sodickson, chair of the National Institutes of Health study section on biomedical imaging technology, is credited with founding the field of parallel imaging, which allows distributed arrays of detectors to gather MR images at previously inaccessible speeds.
In his lecture, he predicted that imaging studies will become less like still photography and more like streaming video. Scanners that acquire information from many different angles at the same time will capture patients’ data continuously and algorithms will select it as needed to reconstruct images for specific purposes.
“You can see a moving abdomen and the flow of contrast,” said Dr. Sodickson, vice chair for research in the Department of Radiology, director of the Bernard and Irene Schwartz Center for Biomedical Imaging, and a professor of radiology, physiology and neuroscience at NYU School of Medicine, in the NYU Langone Health System in New York City. “You can freeze the heart and look at respiratory function, or track the coronary arteries and freeze them at any point you like.”
An Alphabet Soup of Algorithms
This revolution in physical modeling is changing the way radiologists interpret image information and connect organ-level maps to underlying cellular architecture and molecular composition.
At the same time, an alphabet soup of AI algorithms are deriving new information from sometimes very low quality image data, and may someday change the way imaging devices are designed, Dr. Sodickson said. In fact, recent implementations of AI for image reconstruction have begun to resemble the neural processing of complex, continuous sensory data streams.
He compared this change to the difference between looking at a single image, which gives a single stream of visual information, and being at a live concert, which generates multiple streams of information that the brain quickly sorts through, eliminating unnecessary information and pinpointing what to focus on.
Dr. Sodickson predicted that techniques such as MR fingerprinting (which isolates unique information in MR images that might be used to identify specific tissue types, cell types, or diseases) will take image information out of the realm of subjectivity, depending on the skill of the reader and the technician and the features of the scanner, and make it as objective as a blood test.
MR fingerprints will be available in a database and radiologists will look for a match to a specific patient’s information, just as police identify criminals by running their fingerprints against the FBI database.
Where do these changes leave radiologists? In a good place, Dr. Sodickson said.
He suggested that radiologists begin thinking of themselves as “information innovators,” due to their expertise in image data acquisition. The field is already headed in that direction, said Dr. Sodickson, pointing to the vast increase in sessions on machine learning and AI offered at major radiological meetings — including RSNA — in the past year.
“It’s a turbulent age for imaging — there is no doubt,” Dr. Sodickson said, “But I hope I have convinced you it is a golden age for innovation.”