Dubos Johanna, Park Sang Kyoon, Vlasova Roza, Prieto Juan Carlos, Styner Martin
Dept Psychiatry, University of North Carolina, Chapel Hill, NC, USA.
Dept Computer Science, University of North Carolina, Chapel Hill, NC, USA.
Proc SPIE Int Soc Opt Eng. 2023 Feb;12464. doi: 10.1117/12.2654470. Epub 2023 Apr 3.
In the last decade, investigating white matter microstructure and connectivity via diffusion MRI (dmri) has become a crucial cornerstone in neuroimaging studies. However, even modern dmri sequences have inherently a low signal-to-noise ratio and long acquisition times, depending on the spatial resolution. Furthermore, many types of artifacts complicate the appropriate analysis of dmri, necessitating appropriate quality control (QC) procedures, including exclusion and/or correction of inappropriate/erroneous dmri data. Our group has been developing and promoting QC procedures and tools to the community to enable appropriate dmri analyses. Since its development in 2011, our DTIPrep QC tool has become a major tool due its ease of use and dmri QC performance. Over the years, novel developments in acquisition and artifact correction methods have led to a need to modernize DTIPrep. Here, we present a novel diffusion MRI analysis environment called with a fully redesigned and significantly enhanced QC module , and its graphical user interface , building on in-house developed code, FSL and dipy. The user interface is designed to be a unified, user friendly tool for thorough QC of dMRI data.Artifacts addressed by include eddy-currents, head motion, bed vibration and pulsation, venetian blind artifacts, slice-wise and gradient-wise intensity inconsistencies, and susceptibility artifacts. It further provides an user interface for visual QC of gradients and automated tractography. In summary, our work presents a novel open-source framework for modern comprehensive dmri QC.
在过去十年中,通过扩散磁共振成像(dmri)研究白质微观结构和连通性已成为神经影像学研究的关键基石。然而,即使是现代的dmri序列,其固有信噪比低且采集时间长,这取决于空间分辨率。此外,多种伪影使dmri的适当分析变得复杂,因此需要适当的质量控制(QC)程序,包括排除和/或校正不适当/错误的dmri数据。我们团队一直在开发并向社区推广QC程序和工具,以实现适当的dmri分析。自2011年开发以来,我们的DTIPrep QC工具因其易用性和dmri QC性能,已成为主要工具。多年来,采集和伪影校正方法的新发展导致需要对DTIPrep进行现代化改进。在此,我们基于内部开发的代码、FSL和dipy,展示了一种名为 的新型扩散磁共振成像分析环境,其具有完全重新设计且显著增强的QC模块 及其图形用户界面 。该用户界面设计为一个统一、用户友好的工具,用于对dMRI数据进行全面的QC。 解决的伪影包括涡流、头部运动、床体振动和脉动、百叶窗伪影、切片方向和梯度方向的强度不一致以及磁化率伪影。它还提供了一个用于梯度视觉QC和自动纤维束成像的用户界面。总之,我们的工作展示了一个用于现代综合dmri QC的新型开源框架。