Journal
2020 IEEE 17TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2020)
Volume -, Issue -, Pages 539-543Publisher
IEEE
DOI: 10.1109/isbi45749.2020.9098582
Keywords
functional MRI; diffusion MRI; white matter; adaptive filtering
Funding
- Swedish Research Council [2018-06689, 2017-04889]
- Center for Industrial Information Technology (CENIIT) at Linkoping University
- Wallenberg AI, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation
- Swedish Research Council [2018-06689, 2017-04889] Funding Source: Swedish Research Council
- Vinnova [2017-04889] Funding Source: Vinnova
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Brain activation mapping using functional MRI (fMRI) based on blood oxygenation level-dependent (BOLD) contrast has been conventionally focused on probing gray matter, the BOLD contrast in white matter having been generally disregarded. Recent results have provided evidence of the functional significance of the white matter BOLD signal, showing at the same time that its correlation structure is highly anisotropic, and related to the diffusion tensor in shape and orientation. This evidence suggests that conventional isotropic Gaussian filters are inadequate for denoising white matter fMRI data, since they are incapable of adapting to the complex anisotropic domain of white matter axonal connections. In this paper we explore a graph-based description of the white matter developed from diffusion MRI data, which is capable of encoding the anisotropy of the domain. Based on this representation we design localized spatial filters that adapt to white matter structure by leveraging graph signal processing principles. The performance of the proposed filtering technique is evaluated on semi-synthetic data, where it shows potential for greater sensitivity and specificity in white matter activation mapping, compared to isotropic filtering.
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