MRI is a useful imaging study to diagnose disease throughout the body. Unfortunately, MRIs are time-consuming, expensive, and uncomfortable for patients. We propose to use the latest developments in artificial intelligence (AI) using a set of techniques called deep learning (DL) to make MRI faster. Specifically, we propose to use DL to ‘synthesize’ or ‘switch one MRI sequence into another, which could speed up the MRI acquisition process by multiple-fold. We will build on our prior work doing this for knee MRI and expand to other body parts, as well as perform external validation of our AI FastMRI tools. We have assembled an interdisciplinary team of experts across diagnostic radiology, imaging informatics and IT, and computer science. Our ultimate goal is to take this promising technology from bench to bedside to improve hospital efficiency and the patient experience.
- Paul Yi, MD
- Vishwa Parekh, PhD
Research Assistants: To be named
- Michael Toland (UMMS Imaging IT)
- Peter Kamel, MD
- Eliot Siegel, MD
- Garzia Zapata, MD