Suppr超能文献

层面弥散编码用于运动和失真校正。

Slice-level diffusion encoding for motion and distortion correction.

机构信息

Centre for the developing Brain, King's College London, London, UK.

Centre for the developing Brain, King's College London, London, UK.

出版信息

Med Image Anal. 2018 Aug;48:214-229. doi: 10.1016/j.media.2018.06.008. Epub 2018 Jun 25.

Abstract

Advances in microstructural modelling are leading to growing requirements on diffusion MRI acquisitions, namely sensitivity to smaller structures and better resolution of the geometric orientations. The resulting acquisitions contain highly attenuated images that present particular challenges when there is motion and geometric distortion. This study proposes to address these challenges by breaking with the conventional one-volume-one-encoding paradigm employed in conventional diffusion imaging using single-shot Echo Planar Imaging. By enabling free choice of the diffusion encoding on the slice level, a higher temporal sampling of slices with low b-value can be achieved. These allow more robust motion correction, and in combination with a second reversed phase-encoded echo, also dynamic distortion correction. These proposed advances are validated on phantom and adult experiments and employed in a study of eight foetal subjects. Equivalence in obtained diffusion quantities with the conventional method is demonstrated as well as benefits in distortion and motion correction. The resulting capability can be combined with any acquisition parameters including multiband imaging and allows application to diffusion MRI studies in general.

摘要

微观结构建模的进展对扩散 MRI 采集提出了越来越高的要求,即对更小结构的敏感性和对几何方向的更好分辨率。由此产生的采集包含高度衰减的图像,在存在运动和几何变形时会带来特殊挑战。本研究旨在通过打破传统的单次激发回波平面成像中常规扩散成像中使用的单个体积一个编码范例来解决这些挑战。通过在切片层面上自由选择扩散编码,可以实现具有低 b 值的切片的更高时间采样。这些可以实现更稳健的运动校正,并且与第二个反转相位编码回波结合使用,还可以实现动态失真校正。这些拟议的改进在体模和成人实验中进行了验证,并在对 8 个胎儿对象的研究中得到了应用。证明了与传统方法获得的扩散量具有等效性,并且在失真和运动校正方面具有优势。所得到的功能可以与任何采集参数(包括多频带成像)相结合,并允许在一般扩散 MRI 研究中应用。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验