标题： 多室扩散-弛豫磁共振信号在球面3D-SHORE基下的表示 显示英文标题

标题： Multi-compartment diffusion-relaxation MR signal representation in the spherical 3D-SHORE basis

摘要： 对来自多参数序列的扩散-弛豫磁共振（MR）信号进行建模最近在社区中引起了极大的兴趣，这是由于新技术显著减少了数据采集时间。 分析扩散-弛豫MR数据的一种优选方法是遵循连续建模原理，该原理使用核来表示组织特征，例如弛豫或扩散特性。 然而，构建具有预定义信号成分的合理字典取决于模型参数空间的采样密度，从而导致每增加一个模型中考虑的组织参数，原子数量呈几何级数增长。 这使得估计信号中每个原子的贡献变得具有挑战性，尤其是考虑到超出单指数衰减的扩散特征时。 本文提出了一种基于简谐振子重建和估计（MC-SHORE）表示的新多室扩散-弛豫MR信号表示方法，与散射采集兼容。 所提出的技术通过$\ell_1$范数在解上施加稀疏性约束，并可根据单个体素中考虑的室来估计微观结构测量值，如原点返回概率和取向分布函数。 该过程已通过计算机模拟和活体数据进行了验证，并且比单室非高斯表示和多室单指数衰减技术更准确地近似了扩散-弛豫MR信号，同时保持字典中原子数量较低。 最终，MC-SHORE过程能够从信号中分离出轴突内/轴突外和自由水的贡献，从而减少在组织边界处可观察到的部分体积效应。 显示英文摘要

摘要： Modelling the diffusion-relaxation magnetic resonance (MR) signal obtained from multi-parametric sequences has recently gained immense interest in the community due to new techniques significantly reducing data acquisition time. A preferred approach for examining the diffusion-relaxation MR data is to follow the continuum modelling principle that employs kernels to represent the tissue features, such as the relaxations or diffusion properties. However, constructing reasonable dictionaries with predefined signal components depends on the sampling density of model parameter space, thus leading to a geometrical increase in the number of atoms per extra tissue parameter considered in the model. That makes estimating the contributions from each atom in the signal challenging, especially considering diffusion features beyond the mono-exponential decay. This paper presents a new Multi-Compartment diffusion-relaxation MR signal representation based on the Simple Harmonic Oscillator-based Reconstruction and Estimation (MC-SHORE) representation, compatible with scattered acquisitions. The proposed technique imposes sparsity constraint on the solution via the $\ell_1$ norm and enables the estimation of the microstructural measures, such as the return-to-the-origin probability, and the orientation distribution function, depending on the compartments considered in a single voxel. The procedure has been verified with in silico and in vivo data and enabled the approximation of the diffusion-relaxation MR signal more accurately than single-compartment non-Gaussian representations and multi-compartment mono-exponential decay techniques, maintaining a low number of atoms in the dictionary. Ultimately, the MC-SHORE procedure allows for separating intra-/extra-axonal and free water contributions from the signal, thus reducing the partial volume effect observable in the boundaries of the tissues.