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

2025-10-17

AI Summary

Multi-shot whole-body diffusion-weighted imaging (DWI) suffers from severe motion artifacts due to respiration and peristalsis, while simultaneously achieving multi-organ coverage, multiple b-values, and high spatial resolution remains clinically challenging. Method: We propose LoSP-Prompt—a novel MRI reconstruction framework that introduces prompt learning into DWI reconstruction for the first time. It integrates high-order local smooth phase modeling with low-rank Hankel matrix optimization and enables end-to-end parameter learning on synthetic abdominal DWI data—without navigators or ground-truth annotations. Contribution/Results: LoSP-Prompt exhibits strong interpretability and cross-organ, cross-scanner generalizability. Validated on over 10,000 clinical images, it doubles spatial resolution, covers seven anatomical regions (e.g., liver, kidney, brain), and achieves radiologist-rated image quality scores of 4–5/5—significantly outperforming state-of-the-art methods.

Technology Category

• Natural Language Processing: Prompt Engineering / Prompting
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Learning to Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Addressing motion artifacts in multi-shot diffusion MRI
• Automating parameter tuning via synthetic data learning
• Enabling high-resolution multi-organ imaging with single model

Innovation

• Physics-informed modeling with locally smooth phase variations
• Synthetic-data-driven prompt learning for parameter tuning
• Single model generalizes across multiple anatomical regions