Enabling Quantitative Cone-Beam CT with a Deep Learning Framework: Monte Carlo-Informed Material Decomposition for Adaptive Radiotherapy
Conventional cone-beam CT (CBCT) on linear accelerators suffers from limited soft-tissue contrast and quantitative inaccuracy, hindering its use for precision radiotherapy tasks. This study aims to develop and clinically validate a deep learning-based materia...
Poster Program · Therapy Physics