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

A b stract: 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 - sl ice, 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 int er - 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 automat ically set via prompt learning trained exclusively on synthetic abdominal DWI data emulatin g physiological motion. The approach eliminates navigator signals and real istic data supervision, providing an interpretable, robust solution for high - resolution multi - organ multi - shot DWI. Ho wever, the ms - iEPI DWI is very sensitive to the inter - shot motion during the data acquisition of each shot ( Figure 1(a - d)) . E ven s light movement on the millimeter scale will cause the significant extra inter - shot phase (motion - induced phase in Fig . All t hese methods can successfully remove image artifacts in brain imaging ( F ig. 1 ( h)), g reatly promot ing applications of multi - shot high - resolution DWI . For the a bdominal tumor diagnosis, such as liver and kidney, ms - iEPI DWI has not been applied well ( F ig. 1 ( l)) . The se movement s bring organ - specific and high - order motion - induced phase s ( Figure 1 ( i, j)), which do not conform to the smooth phase prior assumption made in multi - shot DWI brain imaging .