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Abstract #2781

SUPREM: A Super-Resolution Network for Through-Plane Structure Enhancement in Multi-Contrast MRI Using Disentangled Representation Learning

Yoonseok Choi1, Sunyoung Jung1, Mohammed A. Al-masni2, Daniel Kim1, and Dong-Hyun Kim1
1Yonsei University, Seoul, Korea, Republic of, 2Sejong University, Seoul, Korea, Republic of

Synopsis

Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction, super-resolution

Motivation: Through-plane super-resolution in brain MRI is crucial for reducing discomfort during clinical assessments, yet current methods mainly focus on in-plane enhancements.

Goal(s): This study aims to develop a novel SR method that utilizes disentangled representation learning to improve the fine details of low-resolution T2 by leveraging high-resolution T1.

Approach: We introduce the SUPREM network, which reconstructs high-quality of low-resolution T2 by leveraging structural information decomposed from high-resolution T1, employing a progressive supervised reconstruction and patch-wise contrastive learning.

Results: Our method demonstrates superior structural detail recovery, achieving the highest scores for SSIM and PSNR metrics across multiple datasets, indicating its effectiveness and clinical relevance.

Impact: The proposed method has the potential to reduce discomfort during brain MRI assessments by enhancing through-plane super-resolution, thereby improving structural detail recovery and supporting more accurate diagnostics in clinical practice.

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Keywords