Incorporating outlier information into diffusion-weighted MRI modeling for robust microstructural imaging and structural brain connectivity analyses

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http://hdl.handle.net/10138/338795

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Sairanen , V , Ocampo-Pineda , M , Granziera , C , Schiavi , S & Daducci , A 2022 , ' Incorporating outlier information into diffusion-weighted MRI modeling for robust microstructural imaging and structural brain connectivity analyses ' , NeuroImage , vol. 247 , 118802 . https://doi.org/10.1016/j.neuroimage.2021.118802

Title: Incorporating outlier information into diffusion-weighted MRI modeling for robust microstructural imaging and structural brain connectivity analyses
Author: Sairanen, Viljami; Ocampo-Pineda, Mario; Granziera, Cristina; Schiavi, Simona; Daducci, Alessandro
Contributor organization: University of Helsinki
HUS Children and Adolescents
Kliinisen neurofysiologian yksikkö
Date: 2022-02-15
Language: eng
Number of pages: 13
Belongs to series: NeuroImage
ISSN: 1053-8119
DOI: https://doi.org/10.1016/j.neuroimage.2021.118802
URI: http://hdl.handle.net/10138/338795
Abstract: A B S T R A C T The white matter structures of the human brain can be represented using diffusion-weighted MRI tractography. Unfortunately, tractography is prone to find false-positive streamlines causing a severe decline in its specificity and limiting its feasibility in accurate structural brain connectivity analyses. Filtering algorithms have been pro-posed to reduce the number of invalid streamlines but the currently available filtering algorithms are not suitable to process data that contains motion artefacts which are typical in clinical research. We augmented the Con-vex Optimization Modelling for Microstructure Informed Tractography (COMMIT) algorithm to adjust for these signals drop-out motion artefacts. We demonstrate with comprehensive Monte-Carlo whole brain simulations and in vivo infant data that our robust algorithm is capable of properly filtering tractography reconstructions despite these artefacts. We evaluated the results using parametric and non-parametric statistics and our results demonstrate that if not accounted for, motion artefacts can have severe adverse effects in human brain structural connectivity analyses as well as in microstructural property mappings. In conclusion, the usage of robust filtering methods to mitigate motion related errors in tractogram filtering is highly beneficial, especially in clinical stud-ies with uncooperative patient groups such as infants. With our presented robust augmentation and open-source implementation, robust tractogram filtering is readily available.
Subject: Magnetic resonance imaging
Diffusion
Tractography
Robust
Filtering
Commit
Outlier
Imputation
Structural connectivity
Microstructure
Movement
Weighted modeling
WHITE-MATTER
SPHERICAL-DECONVOLUTION
TRACTOGRAPHY
TENSOR
EXTRACTION
ARTIFACTS
REJECTION
INFANTS
RESTORE
SIGNAL
3112 Neurosciences
3124 Neurology and psychiatry
3126 Surgery, anesthesiology, intensive care, radiology
Peer reviewed: Yes
Rights: cc_by
Usage restriction: openAccess
Self-archived version: publishedVersion


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