MRI-CARE: COMPOSE, ASSESS, AND REFINE WITH EXPERTS FOR 3D MRI RESTORATION

Mixed-artifact MRI restoration requires effective correction without compromising anatomy. Specialized experts offer useful priors, but incomplete correction leaves residual artifacts and mismatched inputs for subsequent experts. We propose MRI-CARE, Compose, Assess, and Refine with Experts, for multi-artifact 3D MRI restoration. An agent combines adaptive expert selection and dose control with verification and learned stopping. Its policy is trained through supervised initialization and trajectory-level reinforcement learning. We further introduce Ruler-guided Residual Refinement (R³): a 3D Ruler compares the endpoint with acceptable MRI exemplars, and a class-agnostic residual map guides a single refinement when needed. HCP experiments show improved restoration fidelity and cortical reconstruction, with R³ providing up to 1.09 dB over expert composition. External evaluation on IXI and ABIDE examines transfer to simulated and real artifacts. Code will be released upon acceptance.