论文标题
多室磁共振指纹的网格方法
An off-the-grid approach to multi-compartment magnetic resonance fingerprinting
论文作者
论文摘要
我们提出了一种新型的数值方法，用于分离图像体素中的多个组织隔室，并在定量的核磁共振（NMR）性质和混合分数（鉴于磁共振指纹（MRF）测量值）进行定量估算。组织的数量，它们的类型或定量特性不是A-Priori，但假定图像由稀疏的隔室组成，并在体素内有线性混合的Bloch磁化响应。多维NMR属性的细网格离散化创建了大型且高度相干的MRF字典，可以挑战（离散）稀疏近似值的数值方法的可扩展性和精度。为了克服这些问题，我们提出了一种使用连续（未污点）Bloch响应模型的稀疏组套索正则化的网格方法。此外，神经网络近似非线性和非分析性BLOCH响应，从而通过提出的算法有效地将梯度的后磁后传播。在模拟和体内健康的大脑MRF数据上测试，我们证明了与基线多区域MRF方法相比，该方案的有效性。
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.
