Real-time motion analytics during brain MRI improve data quality and reduce costs

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Abstract

Head motion systematically distorts clinical and research MRI data. Motion artifacts have biased findings from many structural and functional brain MRI studies. An effective way to remove motion artifacts is to exclude MRI data frames affected by head motion. However, such post-hoc frame censoring can lead to data loss rates of 50% or more in our pediatric patient cohorts. Hence, many scanner operators collect additional ‘buffer data’, an expensive practice that, by itself, does not guarantee sufficient high-quality MRI data for a given participant. Therefore, we developed an easy-to-setup, easy-to-use Framewise Integrated Real-time MRI Monitoring (FIRMM) software suite that provides scanner operators with head motion analytics in real-time, allowing them to scan each subject until the desired amount of low-movement data has been collected. Our analyses show that using FIRMM to identify the ideal scan time for each person can reduce total brain MRI scan times and associated costs by 50% or more.

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Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity.

Rastko Ciric, Daniel H. Wolf, Jonathan D. Power … (2017)

Since initial reports regarding the impact of motion artifact on measures of functional connectivity, there has been a proliferation of participant-level confound regression methods to limit its impact. However, many of the most commonly used techniques have not been systematically evaluated using a broad range of outcome measures. Here, we provide a systematic evaluation of 14 participant-level confound regression methods in 393 youths. Specifically, we compare methods according to four benchmarks, including the residual relationship between motion and connectivity, distance-dependent effects of motion on connectivity, network identifiability, and additional degrees of freedom lost in confound regression. Our results delineate two clear trade-offs among methods. First, methods that include global signal regression minimize the relationship between connectivity and motion, but result in distance-dependent artifact. In contrast, censoring methods mitigate both motion artifact and distance-dependence, but use additional degrees of freedom. Importantly, less effective de-noising methods are also unable to identify modular network structure in the connectome. Taken together, these results emphasize the heterogeneous efficacy of existing methods, and suggest that different confound regression strategies may be appropriate in the context of specific scientific goals.

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Evaluation of ICA-AROMA and alternative strategies for motion artifact removal in resting state fMRI.

Raimon H R Pruim, Maarten Mennes, Jan Buitelaar … (2015)

We proposed ICA-AROMA as a strategy for the removal of motion-related artifacts from fMRI data (Pruim et al., 2015). ICA-AROMA automatically identifies and subsequently removes data-driven derived components that represent motion-related artifacts. Here we present an extensive evaluation of ICA-AROMA by comparing our strategy to a range of alternative strategies for motion-related artifact removal: (i) no secondary motion correction, (ii) extensive nuisance regression utilizing 6 or (iii) 24 realignment parameters, (iv) spike regression (Satterthwaite et al., 2013a), (v) motion scrubbing (Power et al., 2012), (vi) aCompCor (Behzadi et al., 2007; Muschelli et al., 2014), (vii) SOCK (Bhaganagarapu et al., 2013), and (viii) ICA-FIX (Griffanti et al., 2014; Salimi-Khorshidi et al., 2014), without re-training the classifier. Using three different functional connectivity analysis approaches and four different multi-subject resting-state fMRI datasets, we assessed all strategies regarding their potential to remove motion artifacts, ability to preserve signal of interest, and induced loss in temporal degrees of freedom (tDoF). Results demonstrated that ICA-AROMA, spike regression, scrubbing, and ICA-FIX similarly minimized the impact of motion on functional connectivity metrics. However, both ICA-AROMA and ICA-FIX resulted in significantly improved resting-state network reproducibility and decreased loss in tDoF compared to spike regression and scrubbing. In comparison to ICA-FIX, ICA-AROMA yielded improved preservation of signal of interest across all datasets. These results demonstrate that ICA-AROMA is an effective strategy for removing motion-related artifacts from rfMRI data. Our robust and generalizable strategy avoids the need for censoring fMRI data and reduces motion-induced signal variations in fMRI data, while preserving signal of interest and increasing the reproducibility of functional connectivity metrics. In addition, ICA-AROMA preserves the temporal non-artifactual time-series characteristics and limits the loss in tDoF, thereby increasing statistical power at both the subject- and the between-subject analysis level.

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Statistical improvements in functional magnetic resonance imaging analyses produced by censoring high-motion data points.

Joshua S Siegel, Jonathan D. Power, Joseph Dubis … (2014)

Subject motion degrades the quality of task functional magnetic resonance imaging (fMRI) data. Here, we test two classes of methods to counteract the effects of motion in task fMRI data: (1) a variety of motion regressions and (2) motion censoring ("motion scrubbing"). In motion regression, various regressors based on realignment estimates were included as nuisance regressors in general linear model (GLM) estimation. In motion censoring, volumes in which head motion exceeded a threshold were withheld from GLM estimation. The effects of each method were explored in several task fMRI data sets and compared using indicators of data quality and signal-to-noise ratio. Motion censoring decreased variance in parameter estimates within- and across-subjects, reduced residual error in GLM estimation, and increased the magnitude of statistical effects. Motion censoring performed better than all forms of motion regression and also performed well across a variety of parameter spaces, in GLMs with assumed or unassumed response shapes. We conclude that motion censoring improves the quality of task fMRI data and can be a valuable processing step in studies involving populations with even mild amounts of head movement. Copyright © 2013 Wiley Periodicals, Inc.

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Author and article information

Journal

Journal ID (nlm-journal-id): 9215515

Journal ID (pubmed-jr-id): 20498

Journal ID (nlm-ta): Neuroimage

Journal ID (iso-abbrev): Neuroimage

Title: NeuroImage

ISSN (Print): 1053-8119

ISSN (Electronic): 1095-9572

Publication date Nihms-submitted: 1 December 2017

Publication date (Electronic): 10 August 2017

Publication date (Print): 01 November 2017

Publication date PMC-release: 15 December 2017

Volume: 161

Pages: 80-93

Affiliations

[a ]Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA

[b ]Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA

[c ]Program in Occupational Therapy, Washington University, St. Louis, MO, USA

[d ]Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA

[e ]Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, USA

[f ]Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA

[g ]Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA

[h ]Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR, USA

Author notes

[* ]Corresponding author. Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA

[** ]Corresponding author. Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, USA

Article

Manuscript ID: NIHMS922211

DOI: 10.1016/j.neuroimage.2017.08.025

PMC ID: 5731481

PubMed ID: 28803940

SO-VID: 132975be-fee6-4c94-bcb4-627a4b0b548b

License:

This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

Real-time motion analytics during brain MRI improve data quality and reduce costs

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Most cited references 28

Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity.

Evaluation of ICA-AROMA and alternative strategies for motion artifact removal in resting state fMRI.

Statistical improvements in functional magnetic resonance imaging analyses produced by censoring high-motion data points.

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