Imagine radiologists and physicians being able to use a scan to predict pancreatic cancer—three years before a patient actually gets it.

That's the hope in the fast-growing medical imaging industry as artificial intelligence progresses. And it could completely transform the way diseases are predicted, prevented, detected and treated. 

At Cedars-Sinai, that research is already underway with the help of a group of graduate students exclusively focused on medical imaging. Together, they are studying the potential of imaging technology and building the workforce of tomorrow to unleash it.

The two-year Master of Science in Magnetic Resonance in Medicine (MSMRM) program was accredited and launched in 2017, with the first class graduating last year. We spoke to the directors of the program, Debiao Li and Wafa Tawackoli, to learn more about how and why their students are using these tools.

Debiao Li: This is a very unique program. There are only a few programs right now with this kind of specialized training.

There is a huge need in the market, especially as medical imaging becomes more and more popular and more important in both medical research and clinical care. 

DL: The program includes six to seven rotations, including various labs—clinical and industrial. 

We have a strong collaboration with industry, and Siemens is our major partner for research. Each student has a chance to work with two on-site Siemens scientists in the industry rotation on developing new products and industry science. 

In clinical rotations, we cover several different disease areas such as cancer, neurology, musculoskeletal and cardiology, and we work with the clinical imaging department to offer a detailed rundown of how a diagnosis is done based on imaging. 

In Discoveries: Seeing Through the Machine’s Eye

Wafa Tawackoli: What makes us different than other places is we have this imaging facility dedicated to research, where these students are allowed to come down and observe MR scans for different clinical applications.

They actually do phantom studies and organ studies. Students learn how to perform routine MRI calibrations to ensure accuracy of the machines. 

In the Newsroom: New Cardiac MRI Technique Shortens Testing Time, Potentially Increases Diagnostic Accuracy 

WT: We are working on new MRI coils in the research hardware laboratory to make results more readable and usable. We are also working on using MRI systems to make shorter scan times at higher resolutions.

Another major focus of the program is artificial intelligence, which is becoming our strength. We are trying to use computers and algorithms to analyze the images much quicker and more accurately. That's where disease prediction and precision medicine comes in.

One master's student is using AI for research comparing pre-diagnosis health scans of patients who would later go on to develop pancreatic cancer, for example. At the time of the scans, they were considered normal—no disease or no abnormal findings. 

The human eye cannot tell the difference. But when you look at them through artificial intelligence, there are some features that are different than someone who is completely normal. 

WT: AI can detect those differences by analyzing very detailed features. Basically, that research could be teased out to help tell a patient of their risk of developing cancer in the next three years. 

We are also working on a similar project with the Center for Neural Science and Medicine, Department of Neurology and Regenerative Medicine Institute to look at the early stages of ALS.

The future will be AI. Early detection is the key for anything—cancer, cardiovascular disease, brain disease. Imagine if you can detect a disease much earlier before it spreads out. You can have much quicker therapy and probably stop the growth. 

Learn more about the Master of Science in Magnetic Resonance in Medicine program