Iteratively reweighted L1-fitting for model-independent outlier removal and regularization in diffusion MRI

Tobisch, Alexandra; Stöcker, Tony; Groeschel, Samuel; Schultz, Thomas

doi:10.1109/isbi.2016.7493413

Cited by 2 publications

(6 citation statements)

References 9 publications

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“…The results of our previously introduced outlier detection approach IRL1 SHORE are in line with those reported in the corresponding workshop paper . However, carefully optimizing REKINDLE's parameters and model constraints now led to a consistently better performance than with IRL1 SHORE.…”

Section: Resultssupporting

confidence: 83%

“…For research and clinical dMRI data, we compare the proposed method to this state‐of‐the‐art method. Finally, we show that wL1 SHORE outperforms IRL1 SHORE as presented in our previous work …”

Section: Methodssupporting

confidence: 78%

“…Another recent work has learned alternative compact basis functions for outlier detection . In the current article, we present a SHORE‐based approach for dropout detection and imputation that differs significantly from our proceedings paper in that it only performs a single weighted fit, rather than iteratively refining it. We will, therefore, denote the novel approach as non‐iterative, to distinguish it from methods that iterate until some convergence criterion is met.…”

Section: Introductionmentioning

confidence: 99%

“…11,12 Several approaches have addressed this by imputing corrupted signals, so that a fully recovered data set is available for subsequent analysis. This strategy has been followed in the eddy framework of Andersson et al, 5 based on Gaussian Processes, 13 as well as in our own 14 and a parallel work, 15 based on sparse signal reconstruction in the SHORE (Simple Harmonic Oscillatorbased Reconstruction and Estimation) basis. 16 Another recent work has learned alternative compact basis functions for outlier detection.…”

mentioning

confidence: 99%

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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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Section: Resultssupporting

confidence: 83%

Section: Methodssupporting

confidence: 78%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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SHORE‐based detection and imputation of dropout in diffusion MRI

Koch

Жуков

Stöcker

et al. 2019

Magnetic Resonance in Med

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“…Applications such as motion and distortion correction [24], [25] and outlier rejection [26]- [28] require such signal representations for generating rank-reduced predictions to which the input data can be registered or compared. Variational methods for (super-resolution) image reconstruction [29] can also benefit from linear and compact representations as these simplify the required numeric optimization.…”

Section: Introductionmentioning

confidence: 99%

Learning Compact <inline-formula> <tex-math notation="LaTeX">${q}$ </tex-math> </inline-formula>-Space Representations for Multi-Shell Diffusion-Weighted MRI

Christiaens

Cordero‐Grande

Hutter

et al. 2019

IEEE Trans. Med. Imaging

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Diffusion-weighted MRI measures the direction and scale of the local diffusion process in every voxel through its spectrum in q-space, typically acquired in one or more shells. Recent developments in microstructure imaging and multi-tissue decomposition have sparked renewed attention in the radial bvalue dependence of the signal. Applications in motion correction and outlier rejection therefore require a compact linear signal representation that extends over the radial as well as angular domain. Here, we introduce SHARD, a data-driven representation of the q-space signal based on spherical harmonics and a radial decomposition into orthonormal components. This representation provides a complete, orthogonal signal basis, tailored to the spherical geometry of q-space and calibrated to the data at hand. We demonstrate that the rank-reduced decomposition outperforms model-based alternatives in human brain data, whilst faithfully capturing the micro-and meso-structural information in the signal. Furthermore, we validate the potential of joint radialspherical as compared to single-shell representations. As such, SHARD is optimally suited for applications that require lowrank signal predictions, such as motion correction and outlier rejection. Finally, we illustrate its application for the latter using outlier robust regression.

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Iteratively reweighted L1-fitting for model-independent outlier removal and regularization in diffusion MRI

Cited by 2 publications

References 9 publications

SHORE‐based detection and imputation of dropout in diffusion MRI

SHORE‐based detection and imputation of dropout in diffusion MRI

Learning Compact <inline-formula> <tex-math notation="LaTeX">${q}$ </tex-math> </inline-formula>-Space Representations for Multi-Shell Diffusion-Weighted MRI

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