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On Rotational Invariance in Adaptive Spatial Filtering of 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
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.

Place, publisher, year, edition, pages
2006. Vol. 30, no 1, 144-150 p.
Keyword [en]
fMRI; Adaptive filtering; CCA
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-12658DOI: 10.1016/j.neuroimage.2005.09.002ISI: 000235696300014OAI: oai:DiVA.org:liu-12658DiVA: diva2:16788
Available from: 2007-11-07 Created: 2007-11-07 Last updated: 2015-10-09
In thesis
1. Advanced MRI Data Processing
Open this publication in new window or tab >>Advanced MRI Data Processing
2007 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Magnetic resonance imaging (MRI) is a very versatile imaging modality which can be used to acquire several different types of images. Some examples include anatomical images, images showing local brain activation and images depicting different types of pathologies. Brain activation is detected by means of functional magnetic resonance imaging (fMRI). This is useful e.g. in planning of neurosurgical procedures and in neurological research. To find the activated regions, a sequence of images of the brain is collected while a patient or subject alters between resting and performing a task. The variations in image intensity over time are then compared to a model of the variations expected to be found in active parts of the brain. Locations with high correlation between the intensity variations and the model are considered to be activated by the task.

Since the images are very noisy, spatial filtering is needed before the activation can be detected. 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 spatial filtering of fMRI data. One of these is a modification of a previously proposed method, which at each position maximizes the similarity between the filter response and the model. A novel feature of the presented method is rotational invariance, i.e. equal sensitivity to activated regions in different orientations. The other method is based on bilateral filtering. At each position, this method averages pixels which are located in the same type of brain tissue and have similar intensity variation over time.

A method for robust correlation estimation is also presented. This method automatically detects local bursts of noise in a signal and disregards the corresponding signal segments when the correlation is estimated. Hence, the correlation estimate is not affected by the noise bursts. This method is useful not only in analysis of fMRI data, but also in other applications where correlation is used to determine the similarity between signals.

Finally, a method for correcting artifacts in complex MR images is presented. Complex images are used e.g. in the Dixon technique for separate imaging of water and fat. The phase of these images is often affected by artifacts and therefore need correction before the actual water and fat images can be calculated. The presented method for phase correction is based on an image integration technique known as the inverse gradient. The method is shown to provide good results even when applied to images with severe artifacts.

Place, publisher, year, edition, pages
Institutionen för medicinsk teknik, 2007. 91 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1140
Keyword
Magnetic resonance imaging (MRI), functional Magnetic Resonance Imaging (fMRI), spatial filtering, robust correlation estimation, correcting artifacts
National Category
Biomedical Laboratory Science/Technology
Identifiers
urn:nbn:se:liu:diva-10038 (URN)978-91-85895-59-5 (ISBN)
Public defence
2007-11-30, Linden, Hus 421, Campus US, Linköpings universitet, Linköping, 10:15 (English)
Opponent
Supervisors
Available from: 2007-11-07 Created: 2007-11-07 Last updated: 2014-10-08
2. 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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