These findings suggest the method's potential for dramatically reducing gadolinium dose without sacrificing diagnostic quality, according to Dr. Gong.

"Low-dose gadolinium images yield significant untapped clinically useful information that is accessible now by using deep learning and AI," he said.


Now that the researchers have shown that the method is technically possible, they want to study it further in the clinical setting, where Dr. Gong believes it will ultimately find a home.

Future research will include evaluation of the algorithm across a broader range of MRI scanners and with different types of contrast agents.

"We're not trying to replace existing imaging technology," Dr. Gong said. "We're trying to improve it and generate more value from the existing information while looking out for the safety of our patients."

Co-authors are Jonathan Tamir, B.Sc., John Pauly, Max Wintermark, M.D., and Greg Zaharchuk, M.D., Ph.D.

Dr. Gong received an RSNA "Trainee Research Prize – Resident" award for his research.

Note: Copies of RSNA 2018 news releases and electronic images will be available online at RSNA.org/press18 beginning Monday, Nov. 26.

RSNA is an association of over 54,000 radiologists, radiation oncologists, medical physicists and related scientists, promoting excellence in patient care and health care delivery through education, research and technologic innovation. The Society is based in Oak Brook, Ill. (RSNA.org)

Editor's note: The data in these releases may differ from those in the published abstract and those actually presented at the meeting, as researchers continue to update their data right up until the meeting. To ensure you are using the most up-to-date information, please contact us.

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