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Diffusion-Informed Spatial Smoothing of fMRI Data in White Matter Using Spectral Graph Filters
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
Centre of Mathematical Sciences, Lund University, Lund, Sweden.
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning.ORCID iD: 0000-0001-7061-7995
Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, USA; Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, USA.
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2021 (English)In: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 237, article id 118095Article in journal (Refereed) Published
Abstract [en]

Brain activation mapping using functional magnetic resonance imaging (fMRI) has been extensively studied in brain gray matter (GM), whereas in large disregarded for probing white matter (WM). This unbalanced treatment has been in part due to controversies in relation to the nature of the blood oxygenation level-dependent (BOLD) contrast in WM and its detachability. However, an accumulating body of studies has provided solid evidence of the functional significance of the BOLD signal in WM and has revealed that it exhibits anisotropic spatio-temporal correlations and structure-specific fluctuations concomitant with those of the cortical BOLD signal. In this work, we present an anisotropic spatial filtering scheme for smoothing fMRI data in WM that accounts for known spatial constraints on the BOLD signal in WM. In particular, the spatial correlation structure of the BOLD signal in WM is highly anisotropic and closely linked to local axonal structure in terms of shape and orientation, suggesting that isotropic Gaussian filters conventionally used for smoothing fMRI data are inadequate for denoising the BOLD signal in WM. The fundamental element in the proposed method is a graph-based description of WM that encodes the underlying anisotropy observed across WM, derived from diffusion-weighted MRI data. Based on this representation, and leveraging graph signal processing principles, we design subject-specific spatial filters that adapt to a subject’s unique WM structure at each position in the WM that they are applied at. We use the proposed filters to spatially smooth fMRI data in WM, as an alternative to the conventional practice of using isotropic Gaussian filters. We test the proposed filtering approach on two sets of simulated phantoms, showcasing its greater sensitivity and specificity for the detection of slender anisotropic activations, compared to that achieved with isotropic Gaussian filters. We also present WM activation mapping results on the Human Connectome Project’s 100-unrelated subject dataset, across seven functional tasks, showing that the proposed method enables the detection of streamline-like activations within axonal bundles.

Place, publisher, year, edition, pages
Elsevier, 2021. Vol. 237, article id 118095
Keywords [en]
functional MRI, diffusion MRI, white matter, graph signal processing, anisotropy
National Category
Radiology, Nuclear Medicine and Medical Imaging Medical Image Processing
Identifiers
URN: urn:nbn:se:liu:diva-175762DOI: 10.1016/j.neuroimage.2021.118095ISI: 000671134200006PubMedID: 34000402OAI: oai:DiVA.org:liu-175762DiVA, id: diva2:1555680
Funder
Swedish Research Council, 2018-06689Swedish Research Council, 2017- 04889Vinnova, 2018-02230NIH (National Institute of Health), K01DK101631NIH (National Institute of Health), R56AG068261
Note

Funding: McDonnell Center for Systems Neuroscience at Washington University; Swedish Research CouncilSwedish Research CouncilEuropean Commission [2017-04889, 2018-06689]; Royal Physiographic Society of Lund; Thorsten and Elsa Segerfalk Foundation; Hans Werthen Foundation; ITEA3/VINNOVA; Center for Industrial Information Technology (CENIIT) at Linkoping University; BrightFocus FoundationBrightFocus Foundation [A2016172S]; NIHUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA; National Institute of Diabetes and Digestive and Kidney DiseasesUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Diabetes & Digestive & Kidney Diseases (NIDDK) [K01DK101631]; National Institute on AgingUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute on Aging (NIA) [R56AG068261];  [1U54MH091657]

Available from: 2021-05-19 Created: 2021-05-19 Last updated: 2023-03-31
In thesis
1. Modern multimodal methods in brain MRI
Open this publication in new window or tab >>Modern multimodal methods in brain MRI
2023 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Magnetic resonance imaging (MRI) is one of the pillars of modern medical imaging, providing a non-invasive means to generate 3D images of the body with high soft-tissue contrast. Furthermore, the possibilities afforded by the design of MRI sequences enable the signal to be sensitized to a multitude of physiological tissue properties, resulting in a wide variety of distinct MRI modalities for clinical and research use. 

This thesis presents a number of advanced brain MRI applications, which fulfill, to differing extents, two complementary aims. On the one hand, they explore the benefits of a multimodal approach to MRI, combining structural, functional and diffusion MRI, in a variety of contexts. On the other, they emphasize the use of advanced mathematical and computational tools in the analysis of MRI data, such as deep learning, Bayesian statistics, and graph signal processing. 

Paper I introduces an anatomically-adapted extension to previous work in Bayesian spatial priors for functional MRI data, where anatomical information is introduced from a T1-weighted image to compensate for the low anatomical contrast of functional MRI data. 

It has been observed that the spatial correlation structure of the BOLD signal in brain white matter follows the orientation of the underlying axonal fibers. Paper II argues about the implications of this fact on the ideal shape of spatial filters for the analysis of white matter functional MRI data. By using axonal orientation information extracted from diffusion MRI, and leveraging the possibilities afforded by graph signal processing, a graph-based description of the white matter structure is introduced, which, in turn, enables the definition of spatial filters whose shape is adapted to the underlying axonal structure, and demonstrates the increased detection power resulting from their use. 

One of the main clinical applications of functional MRI is functional localization of the eloquent areas of the brain prior to brain surgery. This practice is widespread for various invasive surgeries, but is less common for stereotactic radiosurgery (SRS), a non-invasive surgical procedure wherein tissue is ablated by concentrating several beams of high-energy radiation. Paper III describes an analysis and processing pipeline for functional MRI data that enables its use for functional localization and delineation of organs-at-risk for Elekta GammaKnife SRS procedures. 

Paper IV presents a deep learning model for super-resolution of diffusion MRI fiber ODFs, which outperforms standard interpolation methods in estimating local axonal fiber orientations in white matter. Finally, Paper V demonstrates that some popular methods for anonymizing facial data in structural MRI volumes can be partially reversed by applying generative deep learning models, highlighting one way in which the enormous power of deep learning models can potentially be put to use for harmful purposes. 

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2023. p. 63
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 2307
Keywords
MRI, Functional MRI, Diffusion MRI, Graph signal processing, Deep learning
National Category
Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-192793 (URN)10.3384/9789180751360 (DOI)9789180751353 (ISBN)9789180751360 (ISBN)
Public defence
2023-05-05, Hugo Theorell, building, Campus US, Linköping, 13:15 (English)
Opponent
Supervisors
Note

Funding agencies: CENIIT (Center for industrial information technology) and LiU Cancer, as well as the ITEA/VINNOVA-funded projects IMPACT and ASSIST. Center for Medical Image Science and Visualization (CMIV) at Linköping University.

Available from: 2023-03-31 Created: 2023-03-31 Last updated: 2023-04-06Bibliographically approved

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Abramian, DavidEklund, AndersWestin, Carl-Fredrik

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