Fast and accurate Slicewise OutLIer Detection (SOLID) with informed model estimation for diffusion MRI data

Sairanen, Viljami; Leemans, Alexander; Tax, Chantal M. W.

doi:10.1016/j.neuroimage.2018.07.003

Cited by 54 publications

(50 citation statements)

References 65 publications

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“…Pre-processing of dMRI data involved steps largely in line with recommended steps for standard 3.0T systems, interfacing various tools such as FSL (Smith, et al, 2004), MRtrix3 , and ANTS (Avants, et al, 2011). These steps included: denoising (Veraart, et al, 2016), slicewise outlier detection (SOLID; Sairanen, et al (2018)), and correction for drift (Vos, et al, 2017); motion, eddy, and susceptibility-induced distortions (Andersson, et al, 2003;Andersson and Sotiropoulos, 2016); Gibbs ringing artefact (Kellner, et al, 2016); bias field (Tustison, et al, 2010); and gradient non-linearities (Glasser, et al, 2013;Rudrapatna, et al, 2018). Root mean squared (RMS) displacement from eddy (Andersson and Sotiropoulos, 2016) was used as a summary measure of global head motion.…”

Section: Image Acquisition and Pre-processingmentioning

confidence: 99%

Impact of b-value on estimates of apparent fibre density

Genc

Tax

Raven

et al. 2020

Preprint

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Recent advances in diffusion magnetic resonance imaging (dMRI) analysis techniques have improved our understanding of fibre-specific variations in white matter microstructure.Increasingly, studies are adopting multi-shell dMRI acquisitions to improve the robustness of dMRI-based inferences. However, the impact of b-value choice on the estimation of dMRI measures such as apparent fibre density (AFD) derived from spherical deconvolution is not known. Here, we investigate the impact of b-value sampling scheme on estimates of AFD.First, we performed simulations to assess the correspondence between AFD and simulated intra-axonal signal fraction across multiple b-value sampling schemes. We then studied the impact of sampling scheme on the relationship between AFD and age in a developmental population (n=78) aged 8-18 (mean=12.4, SD=2.9 years) using hierarchical clustering and whole brain fixel-based analyses. Multi-shell dMRI data were collected at 3.0T using ultrastrong gradients (300 mT/m), using 6 diffusion-weighted shells ranging from 0 -6000 s/mm 2 . Simulations revealed that the correspondence between estimated AFD and simulated intra-axonal signal fraction was improved with high b-value shells due to increased suppression of the extra-axonal signal. These results were supported by in vivo data, as sensitivity to developmental age-relationships was improved with increasing bvalue (b=6000 s/mm 2 , median R 2 = .34; b=4000 s/mm 2 , median R 2 = .29; b=2400 s/mm 2 , median R 2 = .21; b=1200 s/mm 2 , median R 2 = .17) in a tract-specific fashion. Overall, estimates of AFD and age-related microstructural development were better characterised at high diffusion-weightings due to improved correspondence with intra-axonal properties.

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Section: Image Acquisition and Pre-processingmentioning

confidence: 99%

Impact of b-value on estimates of apparent fibre density

Genc

Tax

Raven

et al. 2020

Preprint

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“…The dMRI data were corrected for Rician noise bias (Koay et al, 2009a;St-Jean et al, 2016) using estimates of the Gaussian noise standard deviation (Koay et al, 2009b) and the true underlying Rician signal , to determine whether or not any plateau arising in the signal decay curve could be attributed to the effects of the noise floor. The data were checked for signal intensity errors including slice-wise outliers (Sairanen et al, 2018). The STE data were corrected for subject motion by registering the interleaved b0 images to the first b0 image and applying the corresponding transformations to the diffusion-weighted images (DWIs).…”

Section: Preprocessingmentioning

confidence: 99%

The dot-compartment revealed? Diffusion MRI with ultra-strong gradients and spherical tensor encoding in the living human brain

Tax

Szczepankiewicz

Nilsson

et al. 2019

Preprint

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The so-called "dot-compartment" is conjectured in diffusion MRI to represent small spherical spaces, such as cell bodies, in which the diffusion is restricted in all directions. Previous investigations inferred its existence from data acquired with directional diffusion encoding which does not permit a straightforward separation of signals from 'sticks' (axons) and signals from 'dots'. Here we combine isotropic diffusion encoding with ultra-strong diffusion gradients (240 mT/m) to achieve high diffusionweightings with high signal to noise ratio, while suppressing signal arising from anisotropic water compartments with significant mobility along at least one axis (e.g., axons). A dot-compartment, defined to have apparent diffusion coefficient equal to zero and no exchange, would result in a nondecaying signal at very high b-values (b ≳ 7000 s/mm 2 ). With this unique experimental setup, a residual yet slowly decaying, signal above the noise floor for b-values as high as 15000 s/mm 2 was seen clearly in the cerebellar grey matter (GM), and in several white matter (WM) regions to some extent. Upper limits of the dot-signal-fraction were estimated to be ~2% in cerebellar GM and ~0.2% in WM. By relaxing the assumption of zero diffusivity, the signal at high b-values in cerebellar GM could be represented more accurately by an isotropic water pool with a low apparent diffusivity of 0.11 µm $ /ms and a substantial signal fraction of ~7-16%. This remaining signal at high b-values has potential to serve as a novel and simple marker for isotropically-restricted water compartments in cerebellar GM.

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“…There is now a wide variety of diffusion and microstructural models, and it would be a tedious task to design and implement specific robust estimation procedures for each of them. Therefore, the recent approach SOLID estimates a measure of uncertainty that can be used to weight the fitting of arbitrary models . It has been pointed out that excluding DWIs due to outlier removal can effectively lead to a suboptimal sampling scheme, which adversely affects the inference of diffusion measures .…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the recent approach SOLID estimates a measure of uncertainty that can be used to weight the fitting of arbitrary models. 10 It has been pointed out that excluding DWIs due to outlier removal can effectively lead to a suboptimal sampling scheme, which adversely affects the inference of diffusion measures. 11,12 Several approaches have addressed this by imputing corrupted signals, so that a fully recovered data set is available for subsequent analysis.…”

mentioning

confidence: 99%

SHORE‐based detection and imputation of dropout in diffusion MRI

Koch

Жуков

Stöcker

et al. 2019

Magnetic Resonance in Med

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Purpose In diffusion MRI, dropout refers to a strong attenuation of the measured signal that is caused by bulk motion during the diffusion encoding. When left uncorrected, dropout will be erroneously interpreted as high diffusivity in the affected direction. We present a method to automatically detect dropout, and to replace the affected measurements with imputed values. Methods Signal dropout is detected by deriving an outlier score from a simple harmonic oscillator‐based reconstruction and estimation (SHORE) fit of all measurements. The outlier score is defined to detect measurements that are substantially lower than predicted by SHORE in a relative sense, while being less sensitive to measurement noise in cases of weak baseline signal. A second SHORE fit is based on detected inliers only, and its predictions are used to replace outliers. Results Our method is shown to reliably detect and accurately impute dropout in simulated data, and to achieve plausible results in corrupted in vivo dMRI measurements. Computational effort is much lower than with previously proposed alternatives. Conclusions Deriving a suitable outlier score from SHORE results in a fast and accurate method for detection and imputation of dropout in diffusion MRI. It requires measurements with multiple b values (such as multi‐shell or DSI), but is independent from the models used for analysis (such as DKI, NODDI, deconvolution, etc.).

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Fast and accurate Slicewise OutLIer Detection (SOLID) with informed model estimation for diffusion MRI data

Cited by 54 publications

References 65 publications

Impact of b-value on estimates of apparent fibre density

Impact of b-value on estimates of apparent fibre density

The dot-compartment revealed? Diffusion MRI with ultra-strong gradients and spherical tensor encoding in the living human brain

SHORE‐based detection and imputation of dropout in diffusion MRI

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