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Assessing the performance of different DTI motion correction strategies in the presence of EPI distortion correction

机译:在存在EPI失真校正的情况下评估不同DTI运动校正策略的性能

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摘要

Diffusion tensor imaging (DTI) is susceptible to several artifacts due to eddy currents, echo planar imaging (EPI) distortion and subject motion. While several techniques correct for individual distortion effects, no optimal combination of DTI acquisition and processing has been determined. Here, the effects of several motion correction techniques are investigated while also correcting for EPI distortion: prospective correction, using navigation; retrospective correction, using two different popular packages (FSL and TORTOISE); and the combination of both methods. Data from a pediatric group that exhibited incidental motion in varying degrees are analyzed. Comparisons are carried while implementing eddy current and EPI distortion correction. DTI parameter distributions, white matter maps and probabilistic tractography are examined. The importance of prospective correction during data acquisition is demonstrated. In contrast to some previous studies, results also show that the inclusion of retrospective processing also improved ellipsoid fits and both the sensitivity and specificity of group tractographic results, even for navigated data. Matches with anatomical white matter maps are highest throughout the brain for data that have been both navigated and processed using TORTOISE. The inclusion of both prospective and retrospective motion correction with EPI distortion correction is important for DTI analysis, particularly when studying subject populations that are prone to motion.
机译:由于涡电流,回波平面成像(EPI)失真和对象运动,扩散张量成像(DTI)易受多种伪影的影响。尽管有几种技术可以纠正单个失真效果,但尚未确定DTI采集和处理的最佳组合。在这里,研究了几种运动校正技术的效果,同时还校正了EPI失真:使用导航的前瞻性校正;使用两个不同的流行软件包(FSL和TORTOISE)进行追溯校正;以及两种方法的结合。分析来自小儿小组的数据,这些数据表现出不同程度的偶然运动。在实现涡流和EPI失真校正时进行比较。检查DTI参数分布,白质图和概率束摄影。说明了在数据采集期间进行前瞻性校正的重要性。与以前的一些研究相比,结果还显示,回顾性处理的使用还改善了椭球拟合,并且即使对于导航数据,组束线照相结果的敏感性和特异性也得到改善。对于使用TORTOISE导航和处理过的数据,与解剖学上的白质图匹配最高。包含EPI失真校正的前瞻性和回顾性运动校正对DTI分析非常重要,尤其是在研究容易运动的受试者群体时。

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