Motion-Robust Deep Reconstruction for Free-Breathing Cardiac Cine MRI
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arXiv
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| Autori principali: | , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866916030856560640 |
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| author | Yurt, Mahmut Ryu, Kanghyun Li, Zhitao Zhu, Xucheng Mao, Xianglun Janich, Martin Alley, Marcus Setsompop, Kawin Pauly, John Vasanawala, Shreyas Syed, Ali |
| author_facet | Yurt, Mahmut Ryu, Kanghyun Li, Zhitao Zhu, Xucheng Mao, Xianglun Janich, Martin Alley, Marcus Setsompop, Kawin Pauly, John Vasanawala, Shreyas Syed, Ali |
| contents | Conventional cardiac cine MRI relies on breath-hold Cartesian acquisitions, which are vulnerable to motion artifacts and can be uncomfortable or infeasible, particularly for pediatric and other noncompliant patients who cannot reliably hold their breath. Free-breathing radial acquisitions can alleviate these limitations, but robust reconstruction at high acceleration remains challenging due to prominent streak artifacts. To address these limitations, we propose Cine-DL, a clinically oriented framework that couples targeted k-space preprocessing with fast, model-based deep reconstruction. In this pipeline, raw free-breathing radial data undergo retrospective cardiac binning and respiratory gating to resolve cardiac phases and discard motion-corrupted spokes. We then introduce Streak Optimized Coil Compression (SOC), which explicitly preserves cardiac signals while suppressing peripheral interference that typically drives the streak artifacts. The resulting 2D+t cine series is reconstructed with an unrolled network that alternates a ResNet proximal operator with physics-based data consistency updates solved via conjugate gradient. We further employ a memory-efficient training strategy that reduces peak memory usage. We evaluate Cine-DL on free-breathing volunteer data against established baselines (k-t SENSE and iGRASP) and demonstrate clinical translation via hospital deployment on newly acquired patient data. Our experiments show that Cine-DL consistently improves quantitative metrics and visual fidelity, supporting a practical route toward routine, time-sensitive clinical adoption of free-breathing cine MRI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_20687 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Motion-Robust Deep Reconstruction for Free-Breathing Cardiac Cine MRI Yurt, Mahmut Ryu, Kanghyun Li, Zhitao Zhu, Xucheng Mao, Xianglun Janich, Martin Alley, Marcus Setsompop, Kawin Pauly, John Vasanawala, Shreyas Syed, Ali Image and Video Processing Machine Learning Conventional cardiac cine MRI relies on breath-hold Cartesian acquisitions, which are vulnerable to motion artifacts and can be uncomfortable or infeasible, particularly for pediatric and other noncompliant patients who cannot reliably hold their breath. Free-breathing radial acquisitions can alleviate these limitations, but robust reconstruction at high acceleration remains challenging due to prominent streak artifacts. To address these limitations, we propose Cine-DL, a clinically oriented framework that couples targeted k-space preprocessing with fast, model-based deep reconstruction. In this pipeline, raw free-breathing radial data undergo retrospective cardiac binning and respiratory gating to resolve cardiac phases and discard motion-corrupted spokes. We then introduce Streak Optimized Coil Compression (SOC), which explicitly preserves cardiac signals while suppressing peripheral interference that typically drives the streak artifacts. The resulting 2D+t cine series is reconstructed with an unrolled network that alternates a ResNet proximal operator with physics-based data consistency updates solved via conjugate gradient. We further employ a memory-efficient training strategy that reduces peak memory usage. We evaluate Cine-DL on free-breathing volunteer data against established baselines (k-t SENSE and iGRASP) and demonstrate clinical translation via hospital deployment on newly acquired patient data. Our experiments show that Cine-DL consistently improves quantitative metrics and visual fidelity, supporting a practical route toward routine, time-sensitive clinical adoption of free-breathing cine MRI. |
| title | Motion-Robust Deep Reconstruction for Free-Breathing Cardiac Cine MRI |
| topic | Image and Video Processing Machine Learning |
| url | https://arxiv.org/abs/2605.20687 |