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Abstract #3242

Self-Supervised Isotropic MRI Volume Restoration from Complementary Contrast-Plane Acquisition using Two-Phase Fast Score-based Models

Hyeongyu Kim1, Youngjun Song1, Tolga Çukur2, and Dosik Hwang1
1Yonsei University, Seoul, Korea, Republic of, 2Bilkent University, Ankara, Turkey

Synopsis

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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Keywords