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Signal Processing for Robust and Real-Time fMRI With Application to Brain Computer Interfaces
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
2010 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

It is hard to find another research field than functional magnetic resonance imaging (fMRI) that combines so many different areas of research. Without the beautiful physics of MRI we would not have any images to look at in the first place. To get images with good quality it is necessary to fully understand the concepts of the frequency domain. The analysis of fMRI data requires understanding of signal processing and statistics and also knowledge about the anatomy and function of the human brain. The resulting brain activity maps are used by physicians and neurologists in order to plan surgery and to increase their understanding of how the brain works.

This thesis presents methods for signal processing of fMRI data in real-time situations. Real-time fMRI puts higher demands on the signal processing, than conventional fMRI, since all the calculations have to be made in realtime and in more complex situations. The result from the real-time fMRI analysis can for example be used to look at the subjects brain activity in real-time, for interactive planning of surgery or understanding of brain functions. Another possibility is to use the result in order to change the stimulus that is given to the subject, such that the brain and the computer can work together to solve a given task. These kind of setups are often called brain computer interfaces (BCI).

Two BCI are presented in this thesis. In the first BCI the subject was able to balance a virtual inverted pendulum by thinking of activating the left or right hand or resting. In the second BCI the subject in the MR scanner was able to communicate with a person outside the MR scanner, through a communication interface.

Since head motion is common during fMRI experiments it is necessary to apply image registration to align the collected volumes. To do image registration in real-time can be a challenging task, therefore how to implement a volume registration algorithm on a graphics card is presented. The power of modern graphic cards can also be used to save time in the daily clinical work, an example of this is also given in the thesis.

Finally a method for calculating and incorporating a structural based certainty in the analysis of the fMRI data is proposed. The results show that the structural certainty helps to remove false activity that can occur due to head motion, especially at the edge of the brain.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press , 2010. , 130 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1432
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-54040Local ID: LIU-TEK-LIC-2010:3ISBN: 978-91-7393-431-2 (print)OAI: oai:DiVA.org:liu-54040DiVA: diva2:297941
Presentation
2010-03-09, Wranne-salen, CMIV, plan 11, Campus US, Linköpings universitet, Linköping, 14:00 (English)
Opponent
Supervisors
Available from: 2010-02-19 Created: 2010-02-19 Last updated: 2013-08-28Bibliographically approved
List of papers
1. Using Real-Time fMRI to Control a Dynamical System by Brain Activity Classification
Open this publication in new window or tab >>Using Real-Time fMRI to Control a Dynamical System by Brain Activity Classification
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2009 (English)In: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009: 12th International Conference, London, UK, September 20-24, 2009, Proceedings, Part I / [ed] Gerhard Goos, Juris Hartmanis and Jan van Leeuwen, Springer Berlin/Heidelberg, 2009, 1, 1000-1008 p.Conference paper, Published paper (Refereed)
Abstract [en]

We present a method for controlling a dynamical system using real-time fMRI. The objective for the subject in the MR scanner is to balance an inverted pendulum by activating the left or right hand or resting. The brain activity is classified each second by a neural network and the classification is sent to a pendulum simulator to change the force applied to the pendulum. The state of the inverted pendulum is shown to the subject in a pair of VR goggles. The subject was able to balance the inverted pendulum during several minutes, both with real activity and imagined activity. In each classification 9000 brain voxels were used and the response time for the system to detect a change of activity was on average 2-4 seconds. The developments here have a potential to aid people with communication disabilities, such as locked in people. Another future potential application can be to serve as a tool for stroke and Parkinson patients to be able to train the damaged brain area and get real-time feedback for more efficient training.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2009 Edition: 1
Series
Lecture Notes in Computer Science, ISSN 0302-9743 (print), 1611-3349 (online) ; 5761
Keyword
fMRI
National Category
Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-54034 (URN)10.1007/978-3-642-04268-3_123 (DOI)000273617300123 ()978-3-642-04267-6 (ISBN)978-3-642-04268-3 (ISBN)
Conference
MICCAI 2009, 12th International Conference, London, UK, September 20-24, 2009
Projects
CADICS
Note

The original publication is available at www.springerlink.com: Anders Eklund, Henrik Ohlsson, Mats Andersson, Joakim Rydell, Anders Ynnerman and Hans Knutsson, Using Real-Time fMRI to Control a Dynamical System by Brain Activity Classification, 2009, Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009, Lecture Notes in Computer Science, (5761/2009), 1000-1008. http://dx.doi.org/10.1007/978-3-642-04268-3_123 Copyright: Springer Science Business Media http://www.springerlink.com/

Available from: 2010-02-19 Created: 2010-02-19 Last updated: 2015-09-22Bibliographically approved
2. Phase Based Volume Registration Using CUDA
Open this publication in new window or tab >>Phase Based Volume Registration Using CUDA
2010 (English)In: Acoustics Speech and Signal Processing (ICASSP), 2010, IEEE , 2010, 658-661 p.Conference paper, Published paper (Refereed)
Abstract [en]

We present a method for fast phase based registration of volume data for medical applications. As the number of different modalities within medical imaging increases, it becomes more and more important with registration that works for a mixture of modalities. For these applications the phase based registration approach has proven to be superior. Today there seem to be two kinds of groups that work with medical image registration, one that works with refining of the registration algorithms and one that works with implementation of more simple algorithms on graphic cards for speeding up the algorithms. We put the work from these groups together and get the best from both worlds. We achieve a speedup of 10-30 compared to our CPU implementation, which makes fast phase based registration possible for large medical volumes.

Place, publisher, year, edition, pages
IEEE, 2010
Series
IEEE International Conference on Acoustics, Speech and Signal Processing. Proceedings, ISSN 1520-6149 ; 2010
Keyword
Image registration, local phase, CUDA, GPU
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-54035 (URN)10.1109/ICASSP.2010.5495134 (DOI)000287096000159 ()978-1-4244-4295-9 (ISBN)
Conference
The 35th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2010), March 14–19, Dallas, Texas, 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. Anders Eklund, Mats Andersson and Hans Knutsson, Phase Based Volume Registration Using CUDA, 2010, Proceedings of the 35th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2010), 658-661. http://dx.doi.org/10.1109/ICASSP.2010.5495134

Available from: 2010-02-19 Created: 2010-02-19 Last updated: 2013-08-28Bibliographically approved
3. Fast Phase Based Registration for Robust Quantitative MRI
Open this publication in new window or tab >>Fast Phase Based Registration for Robust Quantitative MRI
2010 (English)In: Proceedings of the annual meeting of the International Society for Magnetic Resonance in Medicine (ISMRM 2010), 2010Conference paper, Published paper (Other academic)
Abstract [en]

Quantitative magnetic resonance imaging has the major advantage that it handles absolute measurements of physical parameters. Quantitative MRI can for example be used to estimate the amount of different tissue types in the brain, but other applications are possible. Parameters such as relaxation rates R1 and R2 and proton density (PD) are independent of MR scanner settings and imperfections and hence are directly representative of the underlying tissue characteristics. Brain tissue quantification is an important aid for diagnosis of neurological diseases, such as multiple sclerosis (MS) and dementia. It is applied to estimate the volume of each tissue type, such as white tissue, grey tissue, myelin and cerebrospinal fluid (CSF). Tissue that deviates from normal values can be found automatically using computer aided diagnosis. In order for the quantification to have a clinical value, both the time in the MR scanner and the time for the data analysis have to be minimized. A challenge in MR quantification is to keep the scan time within clinically acceptable limits. The quantification method that we have used is based on the work by Warntjes et al.

National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-54037 (URN)
Conference
ISMRM Joint Annual Meeting, Stockholm, Sweden,1-7 May 2010
Available from: 2010-02-19 Created: 2010-02-19 Last updated: 2013-08-28Bibliographically approved
4. A Brain Computer Interface for Communication Using Real-Time fMRI
Open this publication in new window or tab >>A Brain Computer Interface for Communication Using Real-Time fMRI
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2010 (English)In: Proceedings of the 20th International Conference on Pattern Recognition, Los Alamitos, CA, USA: IEEE Computer Society, 2010, 3665-3669 p.Conference paper, Published paper (Refereed)
Abstract [en]

We present the first step towards a brain computer interface (BCI) for communication using real-time functional magnetic resonance imaging (fMRI). The subject in the MR scanner sees a virtual keyboard and steers a cursor to select different letters that can be combined to create words. The cursor is moved to the left by activating the left hand, to the right by activating the right hand, down by activating the left toes and up by activating the right toes. To select a letter, the subject simply rests for a number of seconds. We can thus communicate with the subject in the scanner by for example showing questions that the subject can answer. Similar BCI for communication have been made with electroencephalography (EEG). The subject then focuses on a letter while different rows and columns of the virtual keyboard are flashing and the system tries to detect if the correct letter is flashing or not. In our setup we instead classify the brain activity. Our system is neither limited to a communication interface, but can be used for any interface where five degrees of freedom is necessary.

Place, publisher, year, edition, pages
Los Alamitos, CA, USA: IEEE Computer Society, 2010
Series
International Conference on Pattern Recognition, ISSN 1051-4651
Keyword
Biomedical MRI, Medical image processing, Real-time systems
National Category
Biomedical Laboratory Science/Technology Control Engineering
Identifiers
urn:nbn:se:liu:diva-54038 (URN)10.1109/ICPR.2010.894 (DOI)978-1-4244-7542-1 (ISBN)
Conference
20th International Conference on Pattern Recognition, Istanbul, Turkey, 23-26 August 2010
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. Anders Eklund, Mats Andersson, Henrik Ohlsson, Anders Ynnerman and Hans Knutsson, A Brain Computer Interface for Communication Using Real-Time fMRI, 2010, Proceedings from the 20th International Conference on Pattern Recognition (ICPR), 3665-3669. http://dx.doi.org/10.1109/ICPR.2010.894

Available from: 2010-02-19 Created: 2010-02-19 Last updated: 2015-09-22Bibliographically approved
5. On Structural Based Certainty for Robust fMRI Analysis
Open this publication in new window or tab >>On Structural Based Certainty for Robust fMRI Analysis
(English)Manuscript (preprint) (Other academic)
Abstract [en]

We present a method for obtaining and using a structural based certainty for robust functional magnetic resonance imaging (fMRI) analysis. In the area of fMRI it is common to see brain activity maps with activity at the edge of the brain. It is however a known fact that activity close to the edge of the brain can be due to head movement, since the voxels close to the edge will have a higher variance if they switch between being outside and inside the brain. To some extent this can be remedied by aligning each volume to a reference volume, by the means of volume registration. However, the problem with fMRI volumes is that the slices in the volume normally are taken at different timepoints, and motion between the slices can occur. We calculate a structural based certainty for each voxel, from a high resolution T1-weighted volume, and incorporate this certainty into the statistical analysis of the fMRI data. We show that our certainty approach removes a lot of false activity, both on simulated data and on real data.

National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-54039 (URN)
Available from: 2010-02-19 Created: 2010-02-19 Last updated: 2013-08-28

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  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
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  • en-GB
  • en-US
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  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
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