NIMARC-MRI: Abdominal HASTE Dataset and a Baseline U-Net Exposing the Synthetic-to-Real Gap in Low-Resource Motion Correction

Abdulrazaq A. Zubair; Nafiu Musa Muhammad; Simeon Krah; Ummasalma Usman Ibrahim; Yusuf Tijjani Abdumumin; Ismail Ismail Tijjani; Ummulkhairi Ibrahim; Alyasaa Anas; Mubaraq Yakubu; Abbas Rabiu Muhammad

arXiv · 2609.19323

NIMARC-MRI: Abdominal HASTE Dataset and a Baseline U-Net Exposing the Synthetic-to-Real Gap in Low-Resource Motion Correction

Abdulrazaq A. Zubair·Nafiu Musa Muhammad·Simeon Krah·Ummasalma Usman Ibrahim·Yusuf Tijjani Abdumumin·Ismail Ismail Tijjani·Ummulkhairi Ibrahim·Alyasaa Anas·Mubaraq Yakubu·Abbas Rabiu Muhammad

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Abstract

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.

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Abdulrazaq A. Zubair, Nafiu Musa Muhammad, Simeon Krah, Ummasalma Usman Ibrahim, Yusuf Tijjani Abdumumin, Ismail Ismail Tijjani, Ummulkhairi Ibrahim, Alyasaa Anas, Mubaraq Yakubu, Abbas Rabiu Muhammad. 2026-09-16. NIMARC-MRI: Abdominal HASTE Dataset and a Baseline U-Net Exposing the Synthetic-to-Real Gap in Low-Resource Motion Correction. https://arxiv.org/abs/2609.19323

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