首次将扩散模型用于磁共振指纹成像重建，提升高加速扫描下的精度。
Denoising Diffusion Probabilistic Models for Magnetic Resonance Fingerprinting
- 基于条件扩散概率模型，从欠采样数据中重建多组织参数图。
- 在真实脑部扫描数据上优于传统深度学习与压缩感知方法。
- 适合需要快速精准定量MRI的临床研究与高加速成像场景。
磁共振指纹成像（MRF）是一种高效定量MRI方法，可从单次加速扫描中映射多种组织特性。然而，在高度加速和欠采样条件下实现准确重建仍具挑战性，这正是缩短扫描时间的关键。尽管深度学习已推动图像重建进展，但扩散模型在医学成像中的应用尚处起步阶段，尤其尚未应用于MRF任务。本文首次提出一种用于MRF图像重建的条件扩散概率模型。在真实脑部扫描数据上的定性和定量对比表明，该方法优于现有深度学习与压缩感知算法。大量消融实验还探讨了提升计算效率的策略。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。