Addressing a systematic vibration artifact in diffusion‐weighted MRI

Gallichan, Daniel; Scholz, J.; Bartsch, Andreas J.; Behrens, Timothy E.J.; Robson, Matthew D.; Miller, Karla L.

doi:10.1002/hbm.20856

Cited by 94 publications

(125 citation statements)

References 14 publications

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“…The FA and gFA values compared here were obtained from the pyramidal tract, oriented rostrocaudally in the brain. If signal artifacts are direction dependent, as shown in previous work (6), the impact on anisotropic measurements of FA/gFA can be different depending on the type of tracts under analysis. In real data, q-space interpolation has the advantage over direction removal of maintaining uniformity of direction distribution on the sphere.…”

Section: Discussionmentioning

confidence: 75%

“…Corrupted images are often found to occur within volumes of acquired data, particularly when using ultrafast sequences such as echo planar imaging (EPI) (1). Among these corruptions are those characterized by large, unpredictable signal variations that may originate from patient motion (2), physiological-related fluctuations (3,4), spiking artifact (5), and magnetic gradients causing patient table vibrations (6,7).…”

Section: Diffusion-weighted Magnetic Resonance Imaging (Dw-mentioning

confidence: 99%

“…Detection methods include identifying outliers following diffusion tensor reconstruction (4,13), or finding local maxima on the Laplacian of DW signals across diffusion gradient samples (q-space) (14). Correction methods include discarding corrupted voxels (4,15) and fitting the corrupted signal using linear regression methods (6). Despite these investigations, no study has determined outlier impact in advanced reconstruction methods, like q-ball imaging (QBI) (16).…”

Section: Diffusion-weighted Magnetic Resonance Imaging (Dw-mentioning

confidence: 99%

“…ODF reconstruction C in direction y, c is summarized in Eq. [6], where Y is the SH of order k and degree m, c are SH coefficients for the HARDI signal, and P k is an order k Legendre polynomial:…”

Section: Estimation Of Diffusion Odf (Qbi) and Its Metricsmentioning

confidence: 99%

“…In addition, corruption is sometimes associated with a specific direction or group of directions (6). As a means of demonstrating whether such factors would limit the functioning of q-space interpolation in any particular instance, a directional algorithm was devised.…”

Section: Correction By Q-space Interpolationmentioning

confidence: 99%

See 4 more Smart Citations

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

Sharman¹,

Cohen‐Adad²,

Descoteaux³

et al. 2011

Magnetic Resonance Imaging

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Purpose: To measure the impact of corrupted images often found to occur in diffusion-weighted magnetic resonance imaging (DW-MRI). To propose a robust method for the correction of outliers, applicable to diffusion tensor imaging (DTI) and q-ball imaging (QBI). Materials and Methods:Monte Carlo simulations were carried out to measure the impact of outliers on DTI and QBI reconstruction in a single voxel. Methods to correct outliers based on q-space interpolation and direction removal were then implemented and validated in real image data.Results: Corruption in a single voxel led to clear variations in DTI and QBI metrics. In real data, the method of q-space interpolation was successful in identifying corrupted voxels and restoring them to values consistent with those of uncorrupted images. Conclusion:For images containing few gradient directions, where outlier removal was either impossible due to limited volumes or resulted in large changes in DTI/QBI metrics, q-space interpolation proved to be the method of choice for image restoration. A simple decision support system is proposed to assist clinicians in the correction of their corrupted DW data.

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Section: Discussionmentioning

confidence: 75%

Section: Diffusion-weighted Magnetic Resonance Imaging (Dw-mentioning

confidence: 99%

Section: Diffusion-weighted Magnetic Resonance Imaging (Dw-mentioning

confidence: 99%

Section: Estimation Of Diffusion Odf (Qbi) and Its Metricsmentioning

confidence: 99%

Section: Correction By Q-space Interpolationmentioning

confidence: 99%

See 3 more Smart Citations

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

Sharman¹,

Cohen‐Adad²,

Descoteaux³

et al. 2011

Magnetic Resonance Imaging

View full text Add to dashboard Cite

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White matter compromise in autism? Differentiating motion confounds from true differences in diffusion tensor imaging

2017

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Scientific abstract Background Common findings from diffusion tensor imaging (DTI) in autism spectrum disorder (ASD) include reduced fractional anisotropy (FA), and increased mean and radial diffusivity (MD, RD) of white matter tracts. However, findings may be confounded by head motion. We examined how group-level motion matching affects DTI comparisons between ASD and typically developing (TD) groups. Methods We included 57 ASD and 50 TD participants, comparing 3 subsets at increasing levels of motion-matching stringency: full sample (FS); quality-controlled (QC); and quantitatively-matched (QM). Groups were compared on diffusivity measures using Tract-Based Spatial Statistics (TBSS) and probabilistic tractography. Two methods for estimating diffusivity were compared: dti-fit and restore. Results TBSS: In set FS, FA was reduced in the ASD compared to the TD group throughout the right hemisphere. This effect was less extensive in set QC and absent in set QM. However, effect sizes remained stable or increased with better quality-control in some regions. Tractography: In set QM, MD was significantly higher in ASD overall and RD was higher in bilateral ILF. Effects were more robust in QM than in FS or QC sets. Effect sizes in several tracts increased with stringent quality matching. Restore improved tensor estimates, with some increases in effect sizes, but did not fully compensate for reduced quality. Conclusions Findings suggest that some previously reported DTI findings for ASD may have been confounded by motion. However, effects in tightly matched subset indicate that tract-specific anomalies probably do exist in ASD. Our results highlight the need for careful quality-control and motion-matching.

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Attention‐deficit/hyperactivity disorder without comorbidity is associated with distinct atypical patterns of cerebral microstructural development

Adisetiyo

Tabesh

Martino

et al. 2013

Human Brain Mapping

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Differential core symptoms and treatment responses are associated with the pure versus comorbid forms of attention-deficit/hyperactivity disorder (ADHD). However, comorbidity has largely been unaccounted for in neuroimaging studies of ADHD. We used diffusional kurtosis imaging to investigate gray matter (GM) and white matter (WM) microstructure of children and adolescents with ADHD (n = 22) compared to typically developing controls (TDC, n = 27) and examined whether differing developmental patterns are related to comorbidity. The ADHD group (ADHD-mixed) consisted of subgroups with and without comorbidity (ADHD-comorbid, n = 11; ADHD-pure, n = 11, respectively). Age-related changes and group differences in cerebral microstructure of the ADHD-mixed group and each ADHD subgroup were compared to TDC. Whole-brain voxel-based analyses with mean kurtosis (MK) and mean diffusivity (MD) metrics were conducted to probe GM and WM. Tract-based spatial statistics analyses of WM were performed with MK, MD, fractional anisotropy, and directional (axial, radial) kurtosis and diffusivity metrics. ADHD-pure patients lacked significant age-related changes in GM and WM microstructure that were observed globally in TDC and had significantly greater WM microstructural complexity than TDC in bilateral frontal and parietal lobes, insula, corpus callosum, and right external and internal capsules. Including ADHD patients with diverse comorbidities in analyses masked these findings. A distinct atypical age-related trajectory and aberrant regional differences in brain microstructure were detected in ADHD without comorbidity. Our results suggest that different phenotypic manifestations of ADHD, defined by the presence or absence of comorbidity, differ in cerebral microstructural markers.

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Addressing a systematic vibration artifact in diffusion‐weighted MRI

Cited by 94 publications

References 14 publications

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

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

White matter compromise in autism? Differentiating motion confounds from true differences in diffusion tensor imaging

Attention‐deficit/hyperactivity disorder without comorbidity is associated with distinct atypical patterns of cerebral microstructural development

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