Novel AI Technique Promises Faster, Safer CT Scans

Researchers at Stony Brook University are developing a new AI-based method that could transform medical imaging, making CT scans faster, safer and more accurate, without needing huge amounts of training data or exposing patients to high doses of radiation.

Supported by Stony Brook University’s AI Seed Grant Program, the team is working with a new deep learning technique called Deep Image Prior (DIP). Unlike most AI tools, DIP doesn’t need to learn from large databases of past patient images. Instead, it builds a high-quality image from scratch, using only the data from the scan in front of it. This means it can still work well even when data is limited or when a patient’s medical history isn’t available.

Senior postdoctoral associate Ziyu Shu, Department of Radiation Oncology, has taken the idea further with a new method called RBP-DIP (short for Residual Back Projection with Deep Image Prior). His approach uses a smart, step-by-step process to improve image clarity, especially in tricky situations, like when a patient moves during a scan, or when doctors need to reduce the amount of radiation.

The team, led by Principal Investigator Xin Qian, clinical assistant professor, Department of Radiation Oncology, is focusing on challenges in current medical imaging systems, and has already seen promising results. Using experimental CT machines, they’ve managed to create clear images using far fewer X-ray angles than usual, without the grainy artifacts or blur that older methods often produce. In some cases, just 51 projections were enough to build a sharp, detailed scan.

Read the full story by Aknita Nagpal on the AI Innovation Institute website.