Revealing hidden material composition via physics-induced deep learning for precision clinical diagnosis

Material-composition information extracted from spectral computed tomography (CT) images facilitates clinical diagnosis by characterizing pathological tissues. However, the complexity of human anatomy and the diversity of tissue types make precise material quantification from single-energy CT (SECT) highly challenging. Here, we propose an X-ray imaging induced multi-material decomposition (MMDX) framework. By integrating the physical principles of photoelectric effect and Compton scattering into a deep-learning network architecture, MMDX achieves superior performance on both phantom and clinical patient datasets, with a 12.92-dB improvement in peak signal-to-noise ratio (PSNR) and a 3.94% increase in volume fraction accuracy. To evaluate the clinical utility of MMDX, we constructed a large-scale clinical CT dataset with 1,637,738 CT images from 7,629 patients. MMDX demonstrates superior performance across 20 downstream tasks, including three diagnosis tasks, two prognosis tasks, and 15 biomarker prediction tasks, showing promise as an accessible AI tool for precision clinical diagnosis.