Magnetic resonance fingerprinting (MRF) is a technique for quantitative estimation of spin- relaxation parameters from magnetic-resonance data. Most current MRF approaches assume that only one tissue is present in each voxel, which neglects intravoxel structure, and may lead to artifacts in the recovered parameter maps at boundaries between tissues. In this work, we propose a multicompartment MRF model that accounts for the presence of multiple tissues per voxel. The model is fit to the data by iteratively solving a sparse linear inverse problem at each voxel, in order to express the measured magnetization signal as a linear combination of a few elements in a precomputed fingerprint dictionary. Thresholding-based methods commonly used for sparse recovery and compressed sensing do not perform well in this setting due to the high local coherence of the dictionary. Instead, we solve this challenging sparse-recovery problem by applying reweighted-𝓁1-norm regularization, implemented using an efficient interior-point method. The proposed approach is validated with simulated data at different noise levels and undersampling factors, as well as with a controlled phantom-imaging experiment on a clinical magnetic-resonance system.

中文翻译：

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多室磁共振指纹图谱

磁共振指纹识别（MRF）是一种根据磁共振数据定量估计自旋弛豫参数的技术。目前大多数 MRF 方法假设每个体素中仅存在一个组织，这忽略了体素内的结构，并且可能导致在组织之间的边界处恢复的参数图中出现伪影。在这项工作中，我们提出了一个多室 MRF 模型，该模型解释了每个体素存在多个组织。该模型通过迭代求解每个体素处的稀疏线性逆问题来拟合数据，以便将测量的磁化信号表示为预先计算的指纹字典中几个元素的线性组合。由于字典的高局部一致性，常用于稀疏恢复和压缩感知的基于阈值的方法在此设置中表现不佳。相反，我们通过应用重新加权-𝓁1-范数正则化来解决这个具有挑战性的稀疏恢复问题，并使用有效的内点方法实现。所提出的方法通过不同噪声水平和欠采样因子下的模拟数据以及临床磁共振系统上的受控体模成像实验进行了验证。