Micro-structure diffusion scalar measures from reduced MRI acquisitions
Micro-structure diffusion scalar measures from reduced MRI acquisitions
复制标题
DOI：
10.1371/journal.pone.0229526
复制
发表时间：
2020-03-09
期刊：
影响因子：
3.7
通讯作者：
Tristan-Vega, Antonio
中科院分区：
文献类型：
--
作者：
Aja-Fernandez, Santiago;De Luis-Garcia, Rodrigo;Tristan-Vega, Antonio
在弥散MRI中，Entrance平均弥散增强器（EAP）提供了相关的微观结构信息和以前被传统技术（如弥散张量成像（DTI））掩盖的白色物质的有意义的描述性图。然而，EAP的直接估计需要笛卡尔q空间的密集采样，涉及大量样本（扩散梯度）以进行适当的重建。在过去十年中，基于EAP的参数表示，已经提出了一系列更有效的技术，但它们仍然意味着获取大量具有不同b值（壳）的扩散梯度。奇怪的是，这已经与努力找到标量措施收集所有的q空间微观结构信息探测在一个单一的指数或一组指数。其中，返回到原点（RTOP），返回到平面（RTPP），返回到轴（RTAP）的概率已迅速获得popularity.In这项工作中，我们提出了所谓的“表观措施使用减少收购”（AMURA），旨在计算标量指数，可以模仿的敏感性，最先进的EAP为基础的措施，微观结构的变化。AMURA通过假设扩散各向异性大致独立于径向方向，大大减少了所需的样本数量和扩散特性估计的计算复杂性。这种简化使我们能够从单壳信息计算封闭形式的表达式，因此AMURA即使在临床实践中也与常用的标准采集协议保持兼容。此外，分析形式的AMURA为基础的措施，而不是迭代，非线性重建无处不在的全面EAP技术，把新引入的表观RTOP，RTPP，和RTAP都强大和有效的计算。
In diffusion MRI, the Ensemble Average diffusion Propagator (EAP) provides relevant micro-structural information and meaningful descriptive maps of the white matter previously obscured by traditional techniques like Diffusion Tensor Imaging (DTI). The direct estimation of the EAP, however, requires a dense sampling of the Cartesian q-space involving a huge amount of samples (diffusion gradients) for proper reconstruction. A collection of more efficient techniques have been proposed in the last decade based on parametric representations of the EAP, but they still imply acquiring a large number of diffusion gradients with different b-values (shells). Paradoxically, this has come together with an effort to find scalar measures gathering all the q-space micro-structural information probed in one single index or set of indices. Among them, the return-to-origin (RTOP), return-to-plane (RTPP), and return-to-axis (RTAP) probabilities have rapidly gained popularity.In this work, we propose the so-called "Apparent Measures Using Reduced Acquisitions" (AMURA) aimed at computing scalar indices that can mimic the sensitivity of state of the art EAP-based measures to micro-structural changes. AMURA drastically reduces both the number of samples needed and the computational complexity of the estimation of diffusion properties by assuming the diffusion anisotropy is roughly independent from the radial direction. This simplification allows us to compute closed-form expressions from single-shell information, so that AMURA remains compatible with standard acquisition protocols commonly used even in clinical practice. Additionally, the analytical form of AMURA-based measures, as opposed to the iterative, non-linear reconstruction ubiquitous to full EAP techniques, turns the newly introduced apparent RTOP, RTPP, and RTAP both robust and efficient to compute.