Learning Compact q-Space Representations for Multi-Shell Diffusion-Weighted MRI
Learning Compact q-Space Representations for Multi-Shell Diffusion-Weighted MRI
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DOI：
10.1109/tmi.2018.2873736
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发表时间：
2019-03-01
影响因子：
10.6
通讯作者：
Tournier, J-Donald
中科院分区：
文献类型：
--
作者：
Christiaens, Daan;Cordero-Grande, Lucilio;Tournier, J-Donald
扩散加权 MRI 通过 q 空间中的频谱来测量每个体素中局部扩散过程的方向和规模，通常在一个或多个壳中获取。微观结构成像和多组织分解的最新发展引起了人们对信号径向 b 值依赖性的重新关注。因此，运动校正和异常值抑制中的应用需要在径向域和角度域上延伸的紧凑线性信号表示。在这里，我们介绍 SHARD，这是一种基于球谐函数和径向分解为正交分量的 q 空间信号的数据驱动表示。这种表示提供了完整的正交信号基础，针对 q 空间的球面几何形状进行定制，并根据手头的数据进行校准。我们证明，降阶分解在人脑数据中优于基于模型的替代方案，同时忠实地捕获信号中的微观和细观结构信息。此外，我们验证了联合径向球形与单壳表示的潜力。因此，SHARD 最适合需要低秩信号预测的应用，例如运动校正和异常值拒绝。最后，我们使用离群稳健回归来说明其在后者中的应用。
Diffusion-weighted MRI measures the direction and scale of the local diffusion process in every voxel through its spectrum in q-space, typically acquired in one or more shells. Recent developments in microstructure imaging and multi-tissue decomposition have sparked renewed attention in the radial b-value dependence of the signal. Applications in motion correction and outlier rejection, therefore, require a compact linear signal representation that extends over the radial as well as angular domain. Here, we introduce SHARD, a data-driven representation of the q-space signal based on spherical harmonics and a radial decomposition into orthonormal components. This representation provides a complete, orthogonal signal basis, tailored to the spherical geometry of q-space, and calibrated to the data at hand. We demonstrate that the rank-reduced decomposition outperforms model-based alternatives in human brain data, while faithfully capturing the micro- and meso-structural information in the signal. Furthermore, we validate the potential of joint radial-spherical as compared with single-shell representations. As such, SHARD is optimally suited for applications that require low-rank signal predictions, such as motion correction and outlier rejection. Finally, we illustrate its application for the latter using outlier robust regression.