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Correlation controlled adaptive filtering for FMRI data
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0002-9091-4724
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0002-9267-2191
2005 (English)In: IFMBE Proceedings: NBC'05 13th Nordic Baltic Conference Biomedical Engineering and Medical Physics / [ed] Ronnie Lundström, Britt Andersson, Helena Grip, Umeå: IFMBE , 2005, 193-194 p.Conference paper, Published paper (Refereed)
Abstract [en]

In analysis of fMRI data, it is common to average neighboring voxels in order to obtain robust estimates of the correlations between voxel timeseries and the model of the signal expected to be present in activated regions. This paper presents a novel method for analysis of fMRI data, which extends this approach by averaging only neighboring voxels whose timeseries have similar correlation coefficients. A comparison between the new method and two other filtering strategies is also presented, and the novel method is shown to have superior ability to discriminate between active and inactive voxels.

Place, publisher, year, edition, pages
Umeå: IFMBE , 2005. 193-194 p.
Series
IFMBE Proceedings, ISSN 1680-0737 ; vol. 9
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:liu:diva-28765Local ID: 13942OAI: oai:DiVA.org:liu-28765DiVA: diva2:249577
Conference
NBC'05 13th Nordic Baltic Conference Biomedical Engineering and Medical Physics, Umeå, Sweden, June 13th - 17th
Available from: 2009-10-09 Created: 2009-10-09 Last updated: 2014-10-08
In thesis
1. Adaptive spatial filtering of fMRI data
Open this publication in new window or tab >>Adaptive spatial filtering of fMRI data
2005 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Functional magnetic resonance imaging (tMRI) is a method for detecting brain regions that are activated when a certain task is carried out. The method is useful in planning of neurosurgical procedures, where knowledge of the exact locations of important functions is needed to avoid damage to these regions. It is also an important tool in neurological research, where it is used to investigate the function of the human brain.

To find the activated regions, a sequence of images of the brain is collected while a patient or subject alters between resting and performing the task. The variations in image intensity over time is then compared to a model of the variations expected to be found in active parts of the brain. Locations where the intensity variations are similar to the model are considered to be activated by the task.

Since the images are very noisy, filtering is needed before the detection of activation. If adaptive filtering is used, i.e. if the filter at each location is adapted to the local neighborhood, very good detection performance can be obtained. This thesis presents two methods for adaptive filtering of fMRI data. One of these is based on canonical correlation analysis (CCA), and is an extension of a previously proposed CCA-based method. As in the old method, CCA is used in each neighborhood to find a spatial fi lter that maximizes the correlation to the model of the intensity variation. A novel feature of the presented method is that it is rotationally invariant, i.e. that it is equally sensitivelo activated regions in different orientations.

The other method is based on bilateral filtering. This method creates spatial filters which averages pixels with similar intensity variation. Since these filters are not optimized to maximize the similarity to the model of activated signals, the risk of declaring inactive pixels as active is lower compared to CCA-based methods.

Place, publisher, year, edition, pages
Linköping: Linköpings universitet, 2005. 57 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1200
Series
LiU-TEK-LIC, 55
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:liu:diva-30122 (URN)15600 (Local ID)91-85457-43-4 (ISBN)15600 (Archive number)15600 (OAI)
Available from: 2009-10-09 Created: 2009-10-09 Last updated: 2013-11-13

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Rydell, JoakimKnutsson, HansBorga, Magnus

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