MR-STAT is an emerging quantitative magnetic resonance imaging technique which aims at obtaining multi-parametric tissue parameter maps from single short scans. It describes the relationship between the spatial-domain tissue parameters and the time-domain measured signal by using a comprehensive, volumetric forward model. The MR-STAT reconstruction solves a large-scale nonlinear problem, thus is very computationally challenging. In previous work, MR-STAT reconstruction using Cartesian readout data was accelerated by approximating the Hessian matrix with sparse, banded blocks, and can be done on high performance CPU clusters with tens of minutes. In the current work, we propose an accelerated Cartesian MR-STAT algorithm incorporating two different strategies: firstly, a neural network is trained as a fast surrogate to learn the magnetization signal not only in the full time-domain but also in the compressed low-rank domain; secondly, based on the surrogate model, the Cartesian MR-STAT problem is re-formulated and split into smaller sub-problems by the alternating direction method of multipliers. The proposed method substantially reduces the computational requirements for runtime and memory. Simulated and in-vivo balanced MR-STAT experiments show similar reconstruction results using the proposed algorithm compared to the previous sparse Hessian method, and the reconstruction times are at least 40 times shorter. Incorporating sensitivity encoding and regularization terms is straightforward, and allows for better image quality with a negligible increase in reconstruction time. The proposed algorithm could reconstruct both balanced and gradient-spoiled in-vivo data within 3 minutes on a desktop PC, and could thereby facilitate the translation of MR-STAT in clinical settings.

中文翻译：

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MR-STAT 加速策略：在 3 分钟内在台式 PC 上实现高分辨率重建

MR-STAT 是一种新兴的定量磁共振成像技术，旨在从单次短扫描中获得多参数组织参数图。它通过使用全面的体积前向模型来描述空间域组织参数和时域测量信号之间的关系。MR-STAT 重建解决了大规模非线性问题，因此在计算上非常具有挑战性。在之前的工作中，使用笛卡尔读出数据的 MR-STAT 重建通过用稀疏带状块近似 Hessian 矩阵来加速，并且可以在高性能 CPU 集群上用数十分钟完成。在目前的工作中，我们提出了一种加速笛卡尔 MR-STAT 算法，它结合了两种不同的策略：首先，训练神经网络作为快速代理，不仅可以在全时域学习磁化信号，还可以在压缩低秩域学习磁化信号；其次，基于代理模型，笛卡尔MR-STAT问题被重新表述，并通过乘子的交替方向方法分解为更小的子问题。所提出的方法大大降低了运行时和内存的计算要求。模拟和体内平衡 MR-STAT 实验表明，与之前的稀疏 Hessian 方法相比，使用该算法的重建结果相似，并且重建时间至少缩短了 40 倍。将灵敏度编码和正则化项结合起来很简单，并且可以在重建时间的增加可以忽略不计的情况下获得更好的图像质量。