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Signal and Anatomical Constraints in Adaptive Filtering of fMRI Data
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.ORCID iD: 0000-0002-9267-2191
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
2007 (English)In: Biomedical Imaging: From Nano to Macro, 2007. ISBI 2007: From Nano to Macro, IEEE , 2007, 432-435 p.Conference paper (Refereed)
Abstract [en]

An adaptive filtering method for fMRI data is presented. The method is related to bilateral filtering, but with a range filter that takes into account local similarities in signal as well as in anatomy. Performance is demonstrated on simulated and real data. It is shown that using both these similarity constraints give better performance than if only one of them is used, and clearly better than standard low-pass filtering.

Place, publisher, year, edition, pages
IEEE , 2007. 432-435 p.
, International Symposium on Biomedical Imaging. Proceedings, ISSN 1945-7928
Keyword [en]
adaptive filters, biomedical MRI, brain, medical signal detection, bilateral filtering, fMRI, low-pass filtering
National Category
Computer Vision and Robotics (Autonomous Systems) Medical Laboratory and Measurements Technologies
URN: urn:nbn:se:liu:diva-12659DOI: 10.1109/ISBI.2007.356881ISI: 000252957300109ISBN: 1-4244-0672-2ISBN: e-1-4244-0672-2OAI: diva2:16789
4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, (ISBI 2007), 12-15 April 2007, Arlington, VA, USA
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.
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1140
Magnetic resonance imaging (MRI), functional Magnetic Resonance Imaging (fMRI), spatial filtering, robust correlation estimation, correcting artifacts
National Category
Biomedical Laboratory Science/Technology
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)
Available from: 2007-11-07 Created: 2007-11-07 Last updated: 2014-10-08

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Borga, MagnusRydell, Joakim
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Computer Vision and Robotics (Autonomous Systems)Medical Laboratory and Measurements Technologies

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