Impact of outliers on diffusion tensor and Q‐ball imaging: Clinical implications and correction strategies

Sharman, Michael; Cohen‐Adad, Julien; Descoteaux, Maxime; Messé, Arnaud; Benali, Habib; Lehericy, Stéphane

doi:10.1002/jmri.22577

Cited by 8 publications

(16 citation statements)

References 42 publications

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“…If such artifacts were present, the corresponding directions were removed (13 participants had at least one direction removed; M = 4, SD = 3.24) before continuing with preprocessing (Sharman et al, 2011). If such artifacts were present, the corresponding directions were removed (13 participants had at least one direction removed; M = 4, SD = 3.24) before continuing with preprocessing (Sharman et al, 2011).…”

Section: Image Processingmentioning

confidence: 99%

The role of the arcuate and middle longitudinal fasciculi in speech perception in noise in adulthood

Tremblay

Perron²,

Deschamps

et al. 2018

Human Brain Mapping

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In this article, we used High Angular Resolution Diffusion Imaging (HARDI) with advanced anatomically constrained particle filtering tractography to investigate the role of the arcuate fasciculus (AF) and the middle longitudinal fasciculus (MdLF) in speech perception in noise in younger and older adults. Fourteen young and 15 elderly adults completed a syllable discrimination task in the presence of broadband masking noise. Mediation analyses revealed few effects of age on white matter (WM) in these fascicles but broad effects of WM on speech perception, independently of age, especially in terms of sensitivity and criterion (response bias), after controlling for individual differences in hearing sensitivity and head size. Indirect (mediated) effects of age on speech perception through WM microstructure were also found, after controlling for individual differences in hearing sensitivity and head size, with AF microstructure related to sensitivity, response bias and phonological priming, and MdLF microstructure more strongly related to response bias. These findings suggest that pathways of the perisylvian region contribute to speech processing abilities, with relatively distinct contributions for the AF (sensitivity) and MdLF (response bias), indicative of a complex contribution of both phonological and cognitive processes to age‐related speech perception decline. These results provide new and important insights into the roles of these pathways as well as the factors that may contribute to elderly speech perception deficits. They also highlight the need for a greater focus to be placed on studying the role of WM microstructure to understand cognitive aging.

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Section: Image Processingmentioning

confidence: 99%

The role of the arcuate and middle longitudinal fasciculi in speech perception in noise in adulthood

Tremblay

Perron²,

Deschamps

et al. 2018

Human Brain Mapping

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“…The usage of spherical interpolation of data has been quite popular in the recent times for [9, 14]. In this scenario while using a weighted version of spherical harmonics only a reduction 0.06 in terms of RMSE could achieved when imputing original signal intensities.…”

Section: Discussionmentioning

confidence: 99%

“…A single artifact in a gradient volume can be detrimental for voxel-wise fitting methods and can lead to increased bias and variance for the methods. It has been shown that a single corrupted gradient volume for a HARDI acquisition can introduce an error of approximately 10% in Fractional Anisotropy (FA) and General Fractional Anisotropy (GFA) [9] using robust spherical harmonics Q-ball imaging [5]. Generally, the practical quality control advice for DW-MRI is to either remove gradient volumes or perform correction using interpolation techniques [9].…”

Section: Introductionmentioning

confidence: 99%

“…It has been shown that a single corrupted gradient volume for a HARDI acquisition can introduce an error of approximately 10% in Fractional Anisotropy (FA) and General Fractional Anisotropy (GFA) [9] using robust spherical harmonics Q-ball imaging [5]. Generally, the practical quality control advice for DW-MRI is to either remove gradient volumes or perform correction using interpolation techniques [9]. Hence, it is critically important for the artifacts to be detected and either removed or imputed for reduction of bias in HARDI methods.…”

Section: Introductionmentioning

confidence: 99%

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SHARD: spherical harmonic-based robust outlier detection for HARDI methods

Nath

Schilling

Hainline

et al. 2018

Medical Imaging 2018: Image Processing

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High Angular Resolution Diffusion Imaging (HARDI) models are used to capture complex intra-voxel microarchitectures. The magnetic resonance imaging sequences that are sensitized to diffusion are often highly accelerated and prone to motion, physiologic, and imaging artifacts. In diffusion tensor imaging, robust statistical approaches have been shown to greatly reduce these adverse factors without human intervention. Similar approaches would be possible with HARDI methods, but robust versions of each distinct HARDI approach would be necessary. To avoid the computational and pragmatic burdens of creating individual robust HARDI analysis variants, we propose a robust outlier imputation model to mitigate outliers prior to traditional HARDI analysis. This model uses a weighted spherical harmonic fit of diffusion weighted magnetic resonance imaging scans to estimate the values which had been corrupted during acquisition to restore them. Briefly, spherical harmonics of 6th order were used to generate basis function which were weighted by diffusion signal for detection of outliers. For validation, a single healthy volunteer was scanned for a single session comprising of two scans one without head movement and the other with deliberate head movement at a b-value of 3000 s/mm2 with 64 diffusion weighted directions with a single b0 (5 averages) per scan. The deliberate motion from the volunteer created natural artifacts in the acquisition of one of the scans. The imputation model shows reduction in root mean squared error of the raw signal intensities and improvement for the HARDI method Q-ball in terms of the Angular Correlation Coefficient. The results reveal that there is quantitative and qualitative improvement. The proposed model can be used as general pre-processing model before implementing any HARDI model in general to restore the artifacts which are created because of the outlier diffusion signal in certain gradient volumes.

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“…Although the artifact is documented and methods to avoid, detect, or correct the artifact have been proposed (Farzinfar et al 2013; Gallichan et al 2010; Koch and Finsterbusch 2011; Mohammadi, Nagy, Hutton, et al 2012; Scherrer and Warfield 2012; Sharman et al 2011), few investigators mention inspecting for it in their diffusion MRI studies (Jensen and Helpern 2010; Mohammadi, Keller, et al 2012). Even when mentioned, it is often cursorily discussed as a possible confound for their findings (Aso et al 2013; Budde et al 2011; Mohammadi, Nagy, Möller, et al 2012; Mohammadi et al 2013; Rapacchi et al 2011; K.…”

Section: Introductionmentioning

confidence: 99%

Investigation of vibration‐induced artifact in clinical diffusion‐weighted imaging of pediatric subjects

Berl

Walker

Modi

et al. 2015

Human Brain Mapping

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It has been reported that mechanical vibrations of the MRI scanner could produce spurious signal dropouts in diffusion-weighted images resulting in artifactual anisotropy in certain regions of the brain with red appearance in the Directionally Encoded Color maps. We performed a review of the frequency of this artifact across pediatric studies, noting differences by scanner manufacturer, acquisition protocol, as well as weight and position of the subject. We also evaluated the ability of automated and quantitative methods to detect this artifact. We found that the artifact may be present in over 50% of data in certain protocols and is not limited to one scanner manufacturer. While a specific scanner had the highest incidence, low body weight and positioning were also associated with appearance of the artifact for both scanner types evaluated, making children potentially more susceptible than adults. Visual inspection remains the best method for artifact identification. Software for automated detection showed very low sensitivity (10%). The artifact may present inconsistently in longitudinal studies. We discuss a published case report that has been widely cited and used as evidence to set policy about diagnostic criteria for determining vegetative state. That report attributed longitudinal changes in anisotropy to white matter plasticity without considering the possibility that the changes were caused by this artifact. Our study underscores the need to check for the presence of this artifact in clinical studies, analyzes circumstances for when it may be more likely to occur, and suggests simple strategies to identify and potentially avoid its effects.

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Impact of outliers on diffusion tensor and Q‐ball imaging: Clinical implications and correction strategies

Cited by 8 publications

References 42 publications

The role of the arcuate and middle longitudinal fasciculi in speech perception in noise in adulthood

The role of the arcuate and middle longitudinal fasciculi in speech perception in noise in adulthood

SHARD: spherical harmonic-based robust outlier detection for HARDI methods

Investigation of vibration‐induced artifact in clinical diffusion‐weighted imaging of pediatric subjects

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