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Anatomically Informed Bayesian Spatial Priors for FMRI Analysis
Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Tekniska fakulteten. Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik.
Linköpings universitet, Institutionen för datavetenskap, Statistik och maskininlärning. Linköpings universitet, Filosofiska fakulteten.
Linköpings universitet, Tekniska fakulteten. Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.ORCID-id: 0000-0002-9091-4724
Linköpings universitet, Institutionen för datavetenskap, Statistik och maskininlärning. Linköpings universitet, Filosofiska fakulteten. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Department of Statistics, Stockholm University.
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2020 (Engelska)Ingår i: ISBI 2020: IEEE International Symposium on Biomedical Imaging / [ed] IEEE, IEEE, 2020Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Existing Bayesian spatial priors for functional magnetic resonance imaging (fMRI) data correspond to stationary isotropic smoothing filters that may oversmooth at anatomical boundaries. We propose two anatomically informed Bayesian spatial models for fMRI data with local smoothing in each voxel based on a tensor field estimated from a T1-weighted anatomical image. We show that our anatomically informed Bayesian spatial models results in posterior probability maps that follow the anatomical structure.

Ort, förlag, år, upplaga, sidor
IEEE, 2020.
Serie
IEEE International Symposium on Biomedical Imaging, ISSN 1945-7928, E-ISSN 1945-8452
Nyckelord [en]
Bayesian statistics, functional MRI, activation mapping, adaptive smoothing
Nationell ämneskategori
Medicinsk bildbehandling
Identifikatorer
URN: urn:nbn:se:liu:diva-165856DOI: 10.1109/ISBI45749.2020.9098342ISI: 000578080300208ISBN: 978-1-5386-9330-8 (tryckt)OAI: oai:DiVA.org:liu-165856DiVA, id: diva2:1433261
Konferens
IEEE 17th International Symposium on Biomedical Imaging (ISBI), Iowa City, IA, USA, 3-7 April 2020
Forskningsfinansiär
Vetenskapsrådet, 2017- 04889
Anmärkning

Funding agencies:  Swedish Research CouncilSwedish Research Council [201704889]; Center for Industrial Information Technology (CENIIT) at Linkoping University

Tillgänglig från: 2020-05-29 Skapad: 2020-05-29 Senast uppdaterad: 2023-03-31Bibliografiskt granskad
Ingår i avhandling
1. Modern multimodal methods in brain MRI
Öppna denna publikation i ny flik eller fönster >>Modern multimodal methods in brain MRI
2023 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
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. 

Ort, förlag, år, upplaga, sidor
Linköping: Linköping University Electronic Press, 2023. s. 63
Serie
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 2307
Nyckelord
MRI, Functional MRI, Diffusion MRI, Graph signal processing, Deep learning
Nationell ämneskategori
Medicinsk bildbehandling
Identifikatorer
urn:nbn:se:liu:diva-192793 (URN)10.3384/9789180751360 (DOI)9789180751353 (ISBN)9789180751360 (ISBN)
Disputation
2023-05-05, Hugo Theorell, building, Campus US, Linköping, 13:15 (Engelska)
Opponent
Handledare
Anmärkning

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.

Tillgänglig från: 2023-03-31 Skapad: 2023-03-31 Senast uppdaterad: 2023-04-06Bibliografiskt granskad

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Abramian, DavidSidén, PerKnutsson, HansVillani, MattiasEklund, Anders

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