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Adaptive spatial filtering of fMRI data
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
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: urn:nbn:se:liu:diva-30122Local ID: 15600ISBN: 91-85457-43-4 (print)OAI: oai:DiVA.org:liu-30122DiVA: diva2:250943
Available from: 2009-10-09 Created: 2009-10-09 Last updated: 2013-11-13
List of papers
1. Dimensionality and degrees of freedom in fMRI data analysis - a comparative study
Open this publication in new window or tab >>Dimensionality and degrees of freedom in fMRI data analysis - a comparative study
2004 (English)In: Biomedical Imaging: Nano to Macro, 2004. IEEE International Symposium on, IEEE , 2004, 988-991 vol.1 p.Conference paper, Published paper (Refereed)
Abstract [en]

Two- and three-dimensional isotropic and anisotropic spatial filters for adaptive fMRI data analysis are compared in terms of activation detection sensitivity and specificity. Evaluations using both real and artificial data are presented. It is shown that three-dimensional anisotropic filters provide superior activation detection performance.

Place, publisher, year, edition, pages
IEEE, 2004
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:liu:diva-22295 (URN)10.1109/ISBI.2004.1398706 (DOI)1481 (Local ID)0-7803-8388-5 (ISBN)1481 (Archive number)1481 (OAI)
Conference
IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Arlington, VA, USA, 15-18 April 2004
Available from: 2009-10-07 Created: 2009-10-07 Last updated: 2014-10-08
2. Correlation controlled bilateral filtering of fMRI data
Open this publication in new window or tab >>Correlation controlled bilateral filtering of fMRI data
2005 (English)In: Proceedings of the International Society for Magnetic Resonance in MEdicine Annual Meeting (ISMRM) 2005, 2005Conference paper, Published paper (Refereed)
Abstract [en]

A novel filtering method for analysis of fMRI data is presented. The method is based on weighted averaging of neighboring voxels whose time-series are, in a sense, similar. A comparison between the new method and other filtering strategies is also presented, and the novel method is shown to have superior ability to discriminate between active and inactive voxels.

National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:liu:diva-28776 (URN)13959 (Local ID)13959 (Archive number)13959 (OAI)
Conference
International Society for Magnetic Resonance in Medicine (ISMRM) 13th Scientific meeting and Exhibition, Miami, Florida, USA, 7-13 May 2005
Available from: 2009-10-09 Created: 2009-10-09 Last updated: 2014-10-08
3. Correlation controlled adaptive filtering for FMRI data
Open this publication in new window or tab >>Correlation controlled adaptive filtering for FMRI data
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
Series
IFMBE Proceedings, ISSN 1680-0737 ; vol. 9
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:liu:diva-28765 (URN)13942 (Local ID)13942 (Archive number)13942 (OAI)
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
4. On Rotational Invariance in Adaptive Spatial Filtering of fMRI Data
Open this publication in new window or tab >>On Rotational Invariance in Adaptive Spatial Filtering of fMRI Data
2006 (English)In: NeuroImage, ISSN 1053-8119, Vol. 30, no 1, 144-150 p.Article in journal (Refereed) Published
Abstract [en]

Canonical correlation analysis (CCA) has previously been shown to work well for detecting neural activity in fMRI data. The reason is that CCA enables simultaneous temporal modeling and adaptive spatial filtering of the data. This article introduces a novel method for adaptive anisotropic filtering using the CCA framework and compares it to a previously proposed method. Isotropic adaptive filtering, which is only able to form isotropic filters of different sizes, is also presented and evaluated. It is shown that a new feature of the proposed method is invariance to the orientation of activated regions, and that the detection performance is superior to both that of the previous method and to isotropic filtering.

Keyword
fMRI; Adaptive filtering; CCA
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-12658 (URN)10.1016/j.neuroimage.2005.09.002 (DOI)000235696300014 ()
Available from: 2007-11-07 Created: 2007-11-07 Last updated: 2015-10-09
5. Adaptive filtering of fMRI data based on correlation and BOLD response similarity
Open this publication in new window or tab >>Adaptive filtering of fMRI data based on correlation and BOLD response similarity
2006 (English)In: Acoustics, Speech and Signal Processing, 2006. ICASSP 2006. Vol. 2, IEEE conference proceedings, 2006, II-997-II-1000 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 time-series and the model of the signal expected to be present in activated regions. We have previously proposed a method where only voxels with similar correlation coefficients are averaged. In this paper we extend this idea, and present a novel method for analysis of fMRI data. In the proposed method, only voxels with similar correlation coefficients and similar time-series are averaged. The proposed method is compared to our previous method and to two well-known filtering strategies, and is shown to have superior ability to discriminate between active and inactive voxels

Place, publisher, year, edition, pages
IEEE conference proceedings, 2006
Series
IEEE International Conference on Acoustics, Speech and Signal Processing. Proceedings, ISSN 1520-6149
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:liu:diva-33494 (URN)10.1109/ICASSP.2006.1660513 (DOI)000245559902208 ()19518 (Local ID)1-4244-0469-X (ISBN)19518 (Archive number)19518 (OAI)
Conference
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006), 14-19 May 2006, Toulouse, France
Available from: 2009-10-09 Created: 2009-10-09 Last updated: 2015-10-09

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  • ieee
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Output format
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