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最佳 DSI 重建参数推荐:更好的 ODF 和更好的连通性。

Optimal DSI reconstruction parameter recommendations: Better ODFs and better connectivity.

机构信息

Sherbrooke Connectivity Imaging Laboratory, Computer Science Department, Université de Sherbrooke, Canada.

MR Clinical Science, Philips Healthcare, Markham, ON, Canada.

出版信息

Neuroimage. 2016 Nov 15;142:1-13. doi: 10.1016/j.neuroimage.2016.07.048. Epub 2016 Jul 30.

Abstract

Diffusion Spectrum Imaging (DSI) has been used for tractography in several publicly available software and a number of recent high impact publications. However, there are several important theoretical, numerical and practical considerations that are often ignored. We revisit the theoretical and state-of-the-art processing steps necessary to go from the DSI signal to the diffusion orientation distribution function (dODF) used by tractography. We show that the parameters in the reconstruction have huge impact on the reconstruction quality and that, while there is no consensus about what they should be, the parameters we most often see in the literature are not optimal. We provide applicable recommendations that improve the accuracy of extracted local orientations and improve accuracy of global connectivity as measured by the Tractometer, a tractography online evaluation system. These recommendations come for "free" as they are applicable to all existing DSI data and do not require a significant increase in computation time. Hence, this paper highlights the do's and dont's of DSI reconstruction.

摘要

扩散张量成像(DSI)已在一些公开可用的软件中用于轨迹追踪,并在一些最近具有高影响力的出版物中得到应用。然而,在实际应用中存在许多被忽视的重要理论、数值和实际问题。我们重新审视了从 DSI 信号到轨迹追踪所使用的扩散方向分布函数(dODF)的理论和最先进的处理步骤。我们表明,重建中的参数对重建质量有巨大影响,虽然对于应该使用哪些参数还没有共识,但我们在文献中最常看到的参数并不是最优的。我们提供了适用的建议,这些建议可以提高局部方向提取的准确性,并提高通过轨迹测量仪(一种轨迹追踪在线评估系统)测量的全局连通性的准确性。这些建议是免费的,因为它们适用于所有现有的 DSI 数据,并且不需要显著增加计算时间。因此,本文强调了 DSI 重建的注意事项。

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