Magnetic Resonance Fingerprinting (MRF) is a time-efficient approach to quantitative MRI, enabling the mapping of multiple tissue properties from a single, accelerated scan. However, achieving accurate reconstructions remains challenging, particularly in highly accelerated and undersampled acquisitions, which are crucial for reducing scan times. While deep learning techniques have advanced image reconstruction, the recent introduction of diffusion models offers new possibilities for imaging tasks, though their application in the medical field is still emerging. Notably, diffusion models have not yet been explored for the MRF problem. In this work, we propose for the first time a conditional diffusion probabilistic model for MRF image reconstruction. Qualitative and quantitative comparisons on in-vivo brain scan data demonstrate that the proposed approach can outperform established deep learning and compressed sensing algorithms for MRF reconstruction. Extensive ablation studies also explore strategies to improve computational efficiency of our approach.

翻译：磁共振指纹成像（MRF）是一种时间高效的定量磁共振成像方法，能够通过单次加速扫描实现多种组织特性的图谱重建。然而，在高度加速和欠采样的数据采集条件下（这对缩短扫描时间至关重要），实现精确重建仍具挑战性。尽管深度学习技术已推动图像重建领域发展，但近期提出的扩散模型为成像任务提供了新的可能性，尽管其在医学领域的应用尚处于起步阶段。值得注意的是，扩散模型尚未在MRF问题中得到探索。本研究首次提出一种用于MRF图像重建的条件扩散概率模型。基于活体脑部扫描数据的定性与定量对比表明，该方法在MRF重建任务中能够超越现有的深度学习与压缩感知算法。此外，通过系统的消融实验，本研究还探索了提升该方法计算效率的优化策略。