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Modelling white matter with spherical deconvolution: How and why?

机译:用球形反卷积模型化白质:如何以及为什么?

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摘要

Since the realization that diffusion MRI can probe the microstructural organization and orientation of biological tissue in vivo and non‐invasively, a multitude of diffusion imaging methods have been developed and applied to study the living human brain. Diffusion tensor imaging was the first model to be widely adopted in clinical and neuroscience research, but it was also clear from the beginning that it suffered from limitations when mapping complex configurations, such as crossing fibres. In this review, we highlight the main steps that have led the field of diffusion imaging to move from the tensor model to the adoption of diffusion and fibre orientation density functions as a more effective way to describe the complexity of white matter organization within each brain voxel. Among several techniques, spherical deconvolution has emerged today as one of the main approaches to model multiple fibre orientations and for tractography applications. Here we illustrate the main concepts and the reasoning behind this technique, as well as the latest developments in the field. The final part of this review provides practical guidelines and recommendations on how to set up processing and acquisition protocols suitable for spherical deconvolution.
机译:自从认识到扩散MRI可以探测体内和非侵入性生物组织的微结构组织和方向以来,已经开发了多种扩散成像方法并将其应用于研究活人脑。扩散张量成像是在临床和神经科学研究中被广泛采用的第一个模型,但是从一开始就很明显,它在绘制复杂构型(例如交叉纤维)时受到限制。在这篇综述中,我们重点介绍了导致弥散成像领域从张量模型转变为采用弥散和纤维方向密度函数的主要步骤,这是描述每个大脑体素中白质组织复杂性的更有效方法。在几种技术中,球形反褶积技术如今已成为建模多种纤维取向和用于束线照相术的主要方法之一。在这里,我们说明了此技术的主要概念和背后的原因,以及该领域的最新发展。这篇综述的最后部分提供了有关如何建立适合球形反卷积的处理和采集协议的实用指南和建议。

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