liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Anatomically Informed Bayesian Spatial Priors for FMRI Analysis
Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Science & Engineering. Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering.
Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences.
Linköping University, Faculty of Science & Engineering. Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0002-9091-4724
Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Department of Statistics, Stockholm University.
Show others and affiliations
2020 (English)In: ISBI 2020: IEEE International Symposium on Biomedical Imaging / [ed] IEEE, IEEE, 2020Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
IEEE, 2020.
Series
IEEE International Symposium on Biomedical Imaging, ISSN 1945-7928, E-ISSN 1945-8452
Keywords [en]
Bayesian statistics, functional MRI, activation mapping, adaptive smoothing
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:liu:diva-165856DOI: 10.1109/ISBI45749.2020.9098342ISI: 000578080300208ISBN: 978-1-5386-9330-8 (print)OAI: oai:DiVA.org:liu-165856DiVA, id: diva2:1433261
Conference
IEEE 17th International Symposium on Biomedical Imaging (ISBI), Iowa City, IA, USA, 3-7 April 2020
Funder
Swedish Research Council, 2017- 04889
Note

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

Available from: 2020-05-29 Created: 2020-05-29 Last updated: 2023-03-31Bibliographically approved
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

Open Access in DiVA

fulltext(599 kB)364 downloads
File information
File name FULLTEXT02.pdfFile size 599 kBChecksum SHA-512
b032e4ba27bd593e108bcebcae0cc49076aec57e62a989ed992f6f6ceb3123a74aa9fe9a64e6cbd00e4c8d503c3328ae8ae988622077fed97ce3cb0d1c27fc4e
Type fulltextMimetype application/pdf

Other links

Publisher's full text

Authority records

Abramian, DavidSidén, PerKnutsson, HansVillani, MattiasEklund, Anders

Search in DiVA

By author/editor
Abramian, DavidSidén, PerKnutsson, HansVillani, MattiasEklund, Anders
By organisation
Center for Medical Image Science and Visualization (CMIV)Faculty of Science & EngineeringDivision of Biomedical EngineeringThe Division of Statistics and Machine LearningFaculty of Arts and Sciences
Medical Image Processing

Search outside of DiVA

GoogleGoogle Scholar
Total: 364 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 509 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf