Keywords: AI Diffusion Models, Image Reconstruction, Isotropic Super resolution
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.
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