NIMARC-MRI：腹部HASTE数据集与暴露低资源运动校正中合成到真实差距的基线U-Net
NIMARC-MRI: Abdominal HASTE Dataset and a Baseline U-Net Exposing the Synthetic-to-Real Gap in Low-Resource Motion Correction
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中文总结 AI 辅助
针对中低收入国家腹部HASTE MRI运动校正缺乏监督数据的问题，提出西非首个公开数据集NIMARC-MRI及7.7M参数U-Net基线，揭示合成到真实运动校正差距。
中文摘要 AI 辅助
呼吸运动会降低中低收入国家腹部T2 HASTE MRI的图像质量，这些国家无法获得厂商运动校正许可，且失败的扫描在常规PACS清理时被删除，从而阻碍了监督训练。为解决这一问题，我们推出了NIMARC-MRI，这是来自西非的首个公开腹部MRI数据集，包含来自尼日利亚一家使用1.5 T西门子扫描仪且未集成运动校正许可的中心的139例清洁HASTE、25例原生运动退化HASTE以及79例配对HASTE-TSE TRIGGER采集。一个具有770万参数的2D U-Net在基于当地基础设施现实所必需的3D一致合成呼吸运动上进行了训练，并通过三层策略进行验证：留出的合成数据、对原生真实运动的盲法放射科医生Likert评分以及跨序列TRIGGER泛化。在合成测试数据上，模型达到了SSIM 0.863 +/- 0.042和PSNR 30.06 +/- 1.80 dB。然而，在原生真实运动上，盲法放射科医生和放射技师评分显示无显著改善（原始平均Likert 4.06 +/- 0.55对比校正后4.04 +/- 0.61，P = 0.914），28%的病例在校正后评分更差。跨序列TRIGGER评估显示SSIM有适度改善（0.404 +/- 0.073对比0.334 +/- 0.055，P < 0.001），但放射科医生未感知到获益。这些发现表明，保守的合成运动无法捕捉临床运动的严重程度，暴露了一个可复现的合成到真实差距。NIMARC-MRI已在Zenodo（此https URL）上发布，采用受控数据使用协议，要求引用。序列元数据和样本子集可公开访问以促进发现，而患者级数据仍仅限于批准的协作者使用。目标是为资源受限环境中的运动校正建立一个可复现的基线。
英文摘要
Respiratory motion degrades abdominal T2 HASTE MRI in low- and middle-income countries where vendor motion-correction licenses are unavailable and failed scans are deleted during routine PACS cleanup, precluding supervised training. To address this, we introduce NIMARC-MRI, the first public abdominal MRI dataset from West Africa, comprising 139 clean HASTE, 25 native motion-degraded HASTE, and 79 paired HASTE-TSE TRIGGER acquisitions from a Nigerian centre operating 1.5 T Siemens scanner without integrated motion-correction licenses. A 2D U-Net with 7.7 million parameters was trained on 3D-consistent synthetic respiratory motion necessitated by local infrastructure reality and validated via a three-tier strategy: held-out synthetic data, blinded radiologist Likert scoring on native real motion, and cross-sequence TRIGGER generalisation. On synthetic test data the model achieved SSIM 0.863 +/- 0.042 and PSNR 30.06 +/- 1.80 dB. On native real motion, however, blinded radiologist and radiographer scores showed no significant improvement (mean Likert 4.06 +/- 0.55 original versus 4.04 +/- 0.61 corrected, P = 0.914), with 28% of cases rated worse after correction. Cross-sequence TRIGGER evaluation showed modest SSIM improvement (0.404 +/- 0.073 versus 0.334 +/- 0.055, P < 0.001) without radiologist-perceived gain. These findings demonstrate that conservative synthetic motion fails to capture clinical motion severity, exposing a reproducible synthetic-to-real gap. NIMARC-MRI is released on Zenodo (https://doi.org) under a controlled data-use agreement requiring citation. Sequence metadata and a sample subset are publicly accessible to facilitate discovery, while patient-level data remain restricted to approved collaborators. The aim is to establish a reproducible baseline for motion correction in resource-constrained settings.
发表机构
- Federal University of Health Sciences, Azare（阿扎雷联邦健康科学大学）
- African Institute for Research Advancement and Innovation (AIRA Africa)（非洲研究与创新促进研究所）
- Bayero University Kano（卡诺拜鲁克大学）
- Medserve Kano Diagnostic Center（卡诺医疗服务中心）
- Radiology, University of Maiduguri Teaching Hospital（迈杜古里教学医院放射科）
- PET Imaging Centre, King’s College London, University of London（伦敦大学国王学院正电子发射断层扫描成像中心）
- Northwest University kano（卡诺西北大学）
机构由 AI 辅助整理，请以论文原文为准。
