Motivation: Self-supervised isotropic volume reconstruction is essential to address the limitations of anisotropic multi-contrast MRI, where differing planar orientations hinder diagnostic pooling. Goal(s): This study aims to generate isotropic, multi-contrast MR volumes from anisotropic data to unify diagnostic information across contrasts. Approach: A self-supervised score-based framework trains on anisotropic images to iteratively refine volumetric estimates across contrasts. Results: Demonstrating superior image quality on brain MRI datasets, the method advances the usability of existing anisotropic multi-contrast protocols in clinical practice. Impact: This method enhances clinical MRI protocols by enabling isotropic multi-contrast volume reconstruction from anisotropic data, improving diagnostic consistency across contrasts. It reduces the need for extended scan times, maximizing data utility and facilitating broader clinical insights in routine practice.
Kim et al. (Tue,) studied this question.