Paper
UEPS: Robust and Efficient MRI Reconstruction
Authors
Xiang Zhou, Hong Shang, Zijian Zhan, Tianyu He, Jintao Meng, Dong Liang
Abstract
Deep unrolled models (DUMs) have become the state of the art for accelerated MRI reconstruction, yet their robustness under domain shift remains a critical barrier to clinical adoption. In this work, we identify coil sensitivity map (CSM) estimation as the primary bottleneck limiting generalization. To address this, we propose UEPS, a novel DUM architecture featuring three key innovations: (i) an Unrolled Expanded (UE) design that eliminates CSM dependency by reconstructing each coil independently; (ii) progressive resolution, which leverages k-space-to-image mapping for efficient coarse-to-fine refinement; and (iii) sparse attention tailored to MRI's 1D undersampling nature. These physics-grounded designs enable simultaneous gains in robustness and computational efficiency. We construct a large-scale zero-shot transfer benchmark comprising 10 out-of-distribution test sets spanning diverse clinical shifts -- anatomy, view, contrast, vendor, field strength, and coil configurations. Extensive experiments demonstrate that UEPS consistently and substantially outperforms existing DUM, end-to-end, diffusion, and untrained methods across all OOD tests, achieving state-of-the-art robustness with low-latency inference suitable for real-time deployment.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.18572v1</id>\n <title>UEPS: Robust and Efficient MRI Reconstruction</title>\n <updated>2026-03-19T07:33:23Z</updated>\n <link href='https://arxiv.org/abs/2603.18572v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.18572v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>Deep unrolled models (DUMs) have become the state of the art for accelerated MRI reconstruction, yet their robustness under domain shift remains a critical barrier to clinical adoption. In this work, we identify coil sensitivity map (CSM) estimation as the primary bottleneck limiting generalization. To address this, we propose UEPS, a novel DUM architecture featuring three key innovations: (i) an Unrolled Expanded (UE) design that eliminates CSM dependency by reconstructing each coil independently; (ii) progressive resolution, which leverages k-space-to-image mapping for efficient coarse-to-fine refinement; and (iii) sparse attention tailored to MRI's 1D undersampling nature. These physics-grounded designs enable simultaneous gains in robustness and computational efficiency. We construct a large-scale zero-shot transfer benchmark comprising 10 out-of-distribution test sets spanning diverse clinical shifts -- anatomy, view, contrast, vendor, field strength, and coil configurations. Extensive experiments demonstrate that UEPS consistently and substantially outperforms existing DUM, end-to-end, diffusion, and untrained methods across all OOD tests, achieving state-of-the-art robustness with low-latency inference suitable for real-time deployment.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='eess.IV'/>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.CV'/>\n <published>2026-03-19T07:33:23Z</published>\n <arxiv:comment>The document contains the main paper and additional experimental details in the supplementary material. Open-source code can be found at: https://github.com/HongShangGroup/UEPS</arxiv:comment>\n <arxiv:primary_category term='eess.IV'/>\n <author>\n <name>Xiang Zhou</name>\n </author>\n <author>\n <name>Hong Shang</name>\n </author>\n <author>\n <name>Zijian Zhan</name>\n </author>\n <author>\n <name>Tianyu He</name>\n </author>\n <author>\n <name>Jintao Meng</name>\n </author>\n <author>\n <name>Dong Liang</name>\n </author>\n </entry>"
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