Physics-Induced Deep Learning Unveils Hidden Tissue Composition for Precision Diagnosis

Summary

Liu et al. present a physics-induced deep-learning approach that extracts tissue composition information from routine single-energy CT scans. By incorporating X-ray imaging principles into model design, this approach improves material characterization and enhances performance across diverse clinical tasks. The framework expands the diagnostic value of standard CT imaging and supports more accessible precision medicine.