Robust High-Resolution Multi-Organ Diffusion MRI Using Synthetic-Data-Tuned Prompt Learning

• URL: http://arxiv.org/abs/2510.15400v1
• Date: Fri, 17 Oct 2025 07:51:35 GMT
• Title: Robust High-Resolution Multi-Organ Diffusion MRI Using Synthetic-Data-Tuned Prompt Learning
• 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,
• Abstract summary: We introduce a reconstruction framework, LoSP-Prompt, that overcomes challenges through physics-informed modeling and synthetic-data-driven prompt learning.<n>We model inter-shot phase variations as a high-order Locally Smooth Phase (LoSP), integrated into a low-rank Hankel matrix reconstruction.
• Score: 29.576774395651984
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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, $p<0.05$), 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.

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