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.

本研究针对磁共振指纹识别（MRF）在高加速和欠采样情况下的精确重建难题，提出了一种新的条件扩散概率模型。这一模型不仅在定性和定量上优于现有的深度学习和压缩感知算法，而且通过大量消融研究探讨了提高计算效率的策略，具有重要的潜在影响。

改进的图像去噪扩散概率模型用于磁共振指纹识别