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跨扫描仪和跨协议弥散磁共振成像数据的调和:基准数据库和算法评估。

Cross-scanner and cross-protocol diffusion MRI data harmonisation: A benchmark database and evaluation of algorithms.

机构信息

Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.

Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom; Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom.

出版信息

Neuroimage. 2019 Jul 15;195:285-299. doi: 10.1016/j.neuroimage.2019.01.077. Epub 2019 Feb 1.

Abstract

Diffusion MRI is being used increasingly in studies of the brain and other parts of the body for its ability to provide quantitative measures that are sensitive to changes in tissue microstructure. However, inter-scanner and inter-protocol differences are known to induce significant measurement variability, which in turn jeopardises the ability to obtain 'truly quantitative measures' and challenges the reliable combination of different datasets. Combining datasets from different scanners and/or acquired at different time points could dramatically increase the statistical power of clinical studies, and facilitate multi-centre research. Even though careful harmonisation of acquisition parameters can reduce variability, inter-protocol differences become almost inevitable with improvements in hardware and sequence design over time, even within a site. In this work, we present a benchmark diffusion MRI database of the same subjects acquired on three distinct scanners with different maximum gradient strength (40, 80, and 300 mT/m), and with 'standard' and 'state-of-the-art' protocols, where the latter have higher spatial and angular resolution. The dataset serves as a useful testbed for method development in cross-scanner/cross-protocol diffusion MRI harmonisation and quality enhancement. Using the database, we compare the performance of five different methods for estimating mappings between the scanners and protocols. The results show that cross-scanner harmonisation of single-shell diffusion data sets can reduce the variability between scanners, and highlight the promises and shortcomings of today's data harmonisation techniques.

摘要

扩散 MRI 因其能够提供对组织微结构变化敏感的定量测量而在大脑和身体其他部位的研究中得到越来越多的应用。然而,已知扫描仪之间和协议之间的差异会导致显著的测量变异性,这反过来又危及获得“真正的定量测量”的能力,并对不同数据集的可靠组合构成挑战。结合来自不同扫描仪和/或在不同时间点采集的数据集可以显著提高临床研究的统计能力,并促进多中心研究。即使仔细协调采集参数可以减少变异性,但随着硬件和序列设计的改进,协议之间的差异几乎不可避免,即使在一个站点内也是如此。在这项工作中,我们展示了一个基准扩散 MRI 数据库,其中包含相同的受试者在三台具有不同最大梯度强度(40、80 和 300 mT/m)的扫描仪上采集的图像,以及“标准”和“最先进”的协议,后者具有更高的空间和角度分辨率。该数据集是跨扫描仪/跨协议扩散 MRI 协调和质量增强方法开发的有用测试平台。使用该数据库,我们比较了五种不同方法在估计扫描仪和协议之间映射的性能。结果表明,单壳扩散数据集的跨扫描仪协调可以减少扫描仪之间的变异性,并突出当今数据协调技术的优缺点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a3a0/6556555/3ba34e48eee8/gr1.jpg

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