多室磁共振指纹的离网方法
An off-the-grid approach to multi-compartment magnetic resonance fingerprinting
摘要：
我们提出了一种新的数值方法来分离图像体素中的多个组织隔间，并定量估计它们的核磁共振（NMR）特性和混合分数，给出了磁共振指纹（MRF）测量。组织的数量、它们的类型或定量特性不是先验已知的，但是假设图像由在体素内具有线性混合布洛赫磁化响应的稀疏隔室组成。多维核磁共振特性的细网格离散化创建了大型且高度一致的MRF字典，这可能会挑战（离散）稀疏逼近的数值方法的可扩展性和精度。为了克服这些问题，我们提出了一个离网的方法配备了一个扩展的概念稀疏组套索正则化稀疏近似连续（非离散化）布洛赫响应模型。此外，非线性和非分析布洛赫响应近似的神经网络，使有效的反向传播的梯度通过所提出的算法。模拟和在体内健康的大脑MRF数据进行测试，我们证明了所提出的计划相比，基线多室MRF方法的有效性。
英文摘要：
We propose a novel numerical approach to separate multiple tissue compartments in image voxels and to estimate quantitatively their nuclear magnetic resonance (NMR) properties and mixture fractions, given magnetic resonance fingerprinting (MRF) measurements. The number of tissues, their types or quantitative properties are not a-priori known, but the image is assumed to be composed of sparse compartments with linearly mixed Bloch magnetisation responses within voxels. Fine-grid discretisation of the multi-dimensional NMR properties creates large and highly coherent MRF dictionaries that can challenge scalability and precision of the numerical methods for (discrete) sparse approximation. To overcome these issues, we propose an off-the-grid approach equipped with an extended notion of the sparse group lasso regularisation for sparse approximation using continuous (non-discretised) Bloch response models. Further, the nonlinear and non-analytical Bloch responses are approximated by a neural network, enabling efficient back-propagation of the gradients through the proposed algorithm. Tested on simulated and in-vivo healthy brain MRF data, we demonstrate effectiveness of the proposed scheme compared to the baseline multicompartment MRF methods.
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