UltimateSynth: MRI Physics for Pan-Contrast AI.

Magnetic resonance imaging (MRI) is commonly used in healthcare for its ability to generate diverse tissue contrasts without ionizing radiation. However, this flexibility complicates downstream analysis, as computational tools are often tailored to specific MRI types and lack generalizability across the full spectrum of scans used in healthcare. Here, we introduce a versatile framework for the development and validation of pan-contrast AI models that can exhaustively cater to the full spectrum of scans achievable with MRI, enabling model deployment across scanner models, scan types, and age groups. Core to our framework is UltimateSynth, a technology that combines tissue physiology and MR physics in synthesizing realistic images across a comprehensive range of contrasts to bolster the AI development life cycle through efficient data labeling, generalizable model training, and thorough performance benchmarking. UltimateSynth is a platform for pan-contrast generalization of contrast-specific tools. We showcase the effectiveness of UltimateSynth by training an off-the-shelf U-Net to generalize anatomical segmentation across over 150,000 unique MRI contrasts, achieving robust tissue volumetric quantification with exceptionally low variability below 2%.