{"ID":2853951,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.15400","arxiv_id":"2510.15400","title":"Robust High-Resolution Multi-Organ Diffusion MRI Using Synthetic-Data-Tuned Prompt Learning","abstract":"Clinical adoption of multi-shot diffusion-weighted magnetic resonance imaging (multi-shot DWI) for body-wide tumor diagnostics is limited by severe motion-induced phase artifacts from respiration, peristalsis, and so on, compounded by multi-organ, multi-slice, multi-direction and multi-b-value complexities. Here, we introduce a reconstruction framework, LoSP-Prompt, that overcomes these challenges through physics-informed modeling and synthetic-data-driven prompt learning. We model inter-shot phase variations as a high-order Locally Smooth Phase (LoSP), integrated into a low-rank Hankel matrix reconstruction. Crucially, the algorithm's rank parameter is automatically set via prompt learning trained exclusively on synthetic abdominal DWI data emulating physiological motion. Validated across 10,000+ clinical images (43 subjects, 4 scanner models, 5 centers), LoSP-Prompt: (1) Achieved twice the spatial resolution of clinical single-shot DWI, enhancing liver lesion conspicuity; (2) Generalized to seven diverse anatomical regions (liver, kidney, sacroiliac, pelvis, knee, spinal cord, brain) with a single model; (3) Outperformed state-of-the-art methods in image quality, artifact suppression, and noise reduction (11 radiologists' evaluations on a 5-point scale, $p\u003c0.05$), achieving 4-5 points (excellent) on kidney DWI, 4 points (good to excellent) on liver, sacroiliac and spinal cord DWI, and 3-4 points (good) on knee and tumor brain. The approach eliminates navigator signals and realistic data supervision, providing an interpretable, robust solution for high-resolution multi-organ multi-shot DWI. Its scanner-agnostic performance signifies transformative potential for precision oncology.","short_abstract":"Clinical adoption of multi-shot diffusion-weighted magnetic resonance imaging (multi-shot DWI) for body-wide tumor diagnostics is limited by severe motion-induced phase artifacts from respiration, peristalsis, and so on, compounded by multi-organ, multi-slice, multi-direction and multi-b-value complexities. Here, we in...","url_abs":"https://arxiv.org/abs/2510.15400","url_pdf":"https://arxiv.org/pdf/2510.15400v1","authors":"[\"Chen Qian\",\"Haoyu Zhang\",\"Junnan Ma\",\"Liuhong Zhu\",\"Qingrui Cai\",\"Yu Wang\",\"Ruibo Song\",\"Lv Li\",\"Lin Mei\",\"Xianwang Jiang\",\"Qin Xu\",\"Boyu Jiang\",\"Ran Tao\",\"Chunmiao Chen\",\"Shufang Chen\",\"Dongyun Liang\",\"Qiu Guo\",\"Jianzhong Lin\",\"Taishan Kang\",\"Mengtian Lu\",\"Liyuan Fu\",\"Ruibin Huang\",\"Huijuan Wan\",\"Xu Huang\",\"Jianhua Wang\",\"Di Guo\",\"Hai Zhong\",\"Jianjun Zhou\",\"Xiaobo Qu\"]","published":"2025-10-17T07:51:35Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\",\"physics.med-ph\"]","methods":"[\"Diffusion Model\",\"Generative Adversarial Network\"]","has_code":false}