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

Abstract

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 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.

Exercise Therapy for Parkinson's Disease: Pedaling Rate Is Related to Changes in Motor Connectivity

“…This is of particular importance in movement disorders such as Parkinson's disease (PD), as patients with these disorders tend to move more than controls, as seen in previous studies (Göttlich et al, 2013;Helmich et al, 2010). Retrospective motion correction is imperfect, because current methods use a volumetric approach (Beall and Lowe, 2014). Accordingly, the effects of motion may be passed to the connectivity analysis.…”

Reprint of: Minimizing noise in pediatric task-based functional MRI; Adolescents with developmental disabilities and typical development

“…This process minimizes the errors that might result from each transformation. Other authors have discussed this in greater detail (e.g., (Beall and Lowe, 2014). These motion correction techniques assume that motion affects the entire volume in a similar fashion and attempt to address first-order motion effects; however, they do not address potential sliceto-slice out of plane motion effects or other second order motion effects.…”

“…Such motion effects may arise from primarily spin history effects (out of plane motion (Friston et al, 1996;Muresan et al, 2005;Yancey et al, 2011), but also B1 inhomogeneity, and B0 inhomogeneity. Semi-prospective (e.g., SimPACE, (Beall and Lowe, 2014)) and retrospective (e.g., SLOMOCO, (Beall and Lowe, 2014) correction methods exist which attempt to address second order motion effects.…”

“…It may also include spike information to effectively de-weight the TR's where excessive volume-to-volume movement occurred, which can be calculated with software packages (including ARTrepair for SPM, 3dDespike for AFNI, and fsl_motion_outliers for FSL). Further these nuisance regressors could be included in the design matrix either at the subject level, or at the group level to account for variance across subjects due to their motion (Beall and Lowe, 2014;Lund et al, 2005;Oakes et al, 2005). Some of these methods are discussed in detail in (Sattherthwaite et al, 2013), especially in the context of resting state connectivity where they are even more important (Fox et al, 2009;Sattherthwaite et al, 2013).…”

“…This may be particularly problematic in pediatric samples (Sattherthwaite et al, 2013). As mentioned previously, there are a number of techniques that attempt to address second order motion effects (e.g., SLOMOCO, (Beall and Lowe, 2014); 2dlmReg and 3dDespike in AFNI; ArtRepair in SPM). Movement within a slice is less problematic as each slice is collected very rapidly (in about 100 ms) (Wilke, 2003).…”