The proposed SUPREM network improves structural detail recovery in MRI using T1 and T2 images, indicating significant potential for clinical accuracy.
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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Choi et al. (2025) studied this question.
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