SimPACE: Generating simulated motion corrupted BOLD data with synthetic-navigated acquisition for the development and evaluation of SLOMOCO: A new, highly effective slicewise motion correction
SimPACE: Generating simulated motion corrupted BOLD data with synthetic-navigated acquisition for the development and evaluation of SLOMOCO: A new, highly effective slicewise motion correction
复制标题
DOI：
10.1016/j.neuroimage.2014.06.038
复制
发表时间：
2014-11-01
期刊：
影响因子：
5.7
通讯作者：
Lowe, Mark J.
中科院分区：
文献类型：
--
作者：
Beall, Erik B.;Lowe, Mark J.
头部运动在功能MRI和静息态MRI中是一个主要问题。现有的方法不能鲁棒地反映真实水平的运动伪影在体内的fMRI数据。主要问题是当前方法假设运动与体积采集同步，因此忽略了体积内运动。这篇手稿涵盖了三个部分，在使用黄金标准的运动损坏的数据，以追求体积内运动校正。首先，我们提出了一种方法来获得运动损坏的数据，准确地知道运动在切片采集水平。该技术在采集真实的BOLD MRI数据时模拟重要的数据采集相关运动伪影。它基于一种新的运动注入脉冲序列，该序列为每个切片独立引入已知运动：模拟前瞻性采集校正（SimPACE）。其次，使用SimPACE采集的数据，我们评估了几种运动校正和表征技术，包括几种常用的BOLD信号和运动参数为基础的指标。最后，我们介绍和评估一种新的，基于切片的运动校正技术。我们的新方法，面向切片的运动校正（SLOMOCO）比体积的方法表现更好，而且，准确地检测独立切片的运动，在这种情况下相当于已知的注入运动。我们表明，SLOMOCO可以建模和校正BOLD数据中几乎所有的运动影响。此外，没有观察到常用的运动度量来鲁棒地识别运动破坏事件，特别是在突然头部移动的最现实的场景中。对于一些流行的度量，即使使用理想的已知切片运动而不是体积参数，性能也很差。这对于依赖于这些度量的方法具有负面影响，例如最近提出的运动校正方法，例如数据审查和全局信号回归。(C)2014爱思唯尔公司All rights reserved.
Head motion in functional MRI and resting-state MRI is a major problem. Existing methods do not robustly reflect the true level of motion artifact for in vivo fMRI data. The primary issue is that current methods assume that motion is synchronized to the volume acquisition and thus ignore intra-volume motion. This manuscript covers three sections in the use of gold-standard motion-corrupted data to pursue an intra-volume motion correction. First, we present a way to get motion corrupted data with accurately known motion at the slice acquisition level. This technique simulates important data acquisition-related motion artifacts while acquiring real BOLD MRI data. It is based on a novel motion-injection pulse sequence that introduces known motion independently for every slice: Simulated Prospective Acquisition CorrEction (SimPACE). Secondly, with data acquired using SimPACE, we evaluate several motion correction and characterization techniques, including several commonly used BOLD signal-and motion parameter-based metrics. Finally, we introduce and evaluate a novel, slice-based motion correction technique. Our novel method, SLice-Oriented MOtion COrrection (SLOMOCO) performs better than the volumetric methods and, moreover, accurately detects the motion of independent slices, in this case equivalent to the known injected motion. We demonstrate that SLOMOCO can model and correct for nearly all effects of motion in BOLD data. Also, none of the commonly used motion metrics was observed to robustly identify motion corrupted events, especially in the most realistic scenario of sudden head movement. For some popular metrics, performance was poor even when using the ideal known slice motion instead of volumetric parameters. This has negative implications for methods relying on these metrics, such as recently proposed motion correction methods such as data censoring and global signal regression. (C) 2014 Elsevier Inc. All rights reserved.