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Advanced MRI Data Processing
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
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 [en]
Magnetic resonance imaging (MRI), functional Magnetic Resonance Imaging (fMRI), spatial filtering, robust correlation estimation, correcting artifacts
National Category
Biomedical Laboratory Science/Technology
Identifiers
URN: urn:nbn:se:liu:diva-10038ISBN: 978-91-85895-59-5 (print)OAI: oai:DiVA.org:liu-10038DiVA: diva2:16792
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
List of papers
1. 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
2. Signal and Anatomical Constraints in Adaptive Filtering of fMRI Data
Open this publication in new window or tab >>Signal and Anatomical Constraints in Adaptive Filtering of fMRI Data
2007 (English)In: Biomedical Imaging: From Nano to Macro, 2007. ISBI 2007: From Nano to Macro, IEEE , 2007, 432-435 p.Conference paper, Published 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
Series
International Symposium on Biomedical Imaging. Proceedings, ISSN 1945-7928
Keyword
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
Identifiers
urn:nbn:se:liu:diva-12659 (URN)10.1109/ISBI.2007.356881 (DOI)000252957300109 ()1-4244-0672-2 (ISBN)e-1-4244-0672-2 (ISBN)
Conference
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
3. Robust Correlation Analysis with an Application to Functional MRI
Open this publication in new window or tab >>Robust Correlation Analysis with an Application to Functional MRI
2008 (English)In: Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE, IEEE conference proceedings, 2008, 453-456 p.Conference paper, Published paper (Refereed)
Abstract [en]

Correlation is often used to measure the similarity between signals and is an important tool in signal and image processing. In some applications it is common that signals are corrupted by local bursts of noise. This adversely affects the performance of signal recognition algorithms. This paper presents a novel correlation estimator, which is robust to locally corrupted signals. The estimator is generalized to multivariate correlation analysis (general linear model, GLM, and canonical correlation analysis, CCA). Synthetic functional MRI data is used to demonstrate the estimator, and its robustness is shown to increase the performance of signal detection.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2008
Series
IEEE International Conference on Acoustics, Speech and Signal Processing. Proceedings, ISSN 1520-6149
Keyword
biomedical MRI, correlation methods, medical signal processing, signal detection, canonical correlation analysis, correlation estimator, general linear model, locally corrupted signals, multivariate correlation analysis, robust correlation analysis, synthetic functional MRI data
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-12660 (URN)10.1109/ICASSP.2008.4517644 (DOI)000257456700114 ()978-1-4244-1483-3 (ISBN)e-978-1-4244-1484-0 (ISBN)
Conference
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2008, 31 March-4 April 2008, Las Vegas, NV, USA
Note

©2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Joakim Rydell, Magnus Borga and Hans Knutsson, Robust Correlation Analysis with an Application to Functional MRI, 2008, IEEE International Conference on Acoustics, Speech and Signal Processing, 2008, Las Vegas, USA, 453-456. http://dx.doi.org/10.1109/ICASSP.2008.4517644

Available from: 2007-11-07 Created: 2007-11-07 Last updated: 2015-10-09
4. Phase Sensitive Reconstruction for Water/Fat Separation in MR Imaging Using Inverse Gradient
Open this publication in new window or tab >>Phase Sensitive Reconstruction for Water/Fat Separation in MR Imaging Using Inverse Gradient
Show others...
2007 (English)In: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007. 10th International Conference, Brisbane, Australia, October 29 - November 2, 2007, Proceedings, Part I / [ed] Nicholas Ayache, Sebastien Ourselin and Anthony Maeder, Springer Berlin/Heidelberg, 2007, 210-218 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a novel method for phase unwrapping for phase sensitive reconstruction in MR imaging. The unwrapped phase is obtained by integrating the phase gradient by solving a Poisson equation. An efficient solver, which has been made publicly available, is used to solve the equation. The proposed method is demonstrated on a fat quantification MRI task that is a part of a prospective study of fat accumulation. The method is compared to a phase unwrapping method based on region growing. Results indicate that the proposed method provides more robust unwrapping. Unlike region growing methods, the proposed method is also straight-forward to implement in 3D.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2007
Series
Lecture Notes in Computer Science, ISSN 0302-9743 (print), 1611-3349 (online) ; 4791
Keyword
MRI
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-12661 (URN)10.1007/978-3-540-75757-3_26 (DOI)000250916000026 ()978-3-540-75756-6 (ISBN)e-978-3-540-75757-3 (ISBN)
Conference
MICCAI 2007, The 10th International Conference on Medical Image Computing and Computer Assisted Interventio, October 29-November 2, Brisbane, Australia
Available from: 2007-11-07 Created: 2007-11-07 Last updated: 2015-10-08

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