Physics-informed self-supervised generation of digital brain MRI phantoms from weighted images using differentiable MRI simulation
Purpose: To develop a physics-informed, self-supervised framework for generating digital brain MRI phantoms directly from conventional weighted MR images without requiring ground-truth parametric maps or anatomical segmentation. Methods: The framework predicts T1, T2, and proton density (PD) maps from T1-, T2-, and PD-...