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Fast Phase Based Registration for Robust Quantitative MRI
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
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: 0000-0002-9091-4724
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.

Place, publisher, year, edition, pages
2010.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-54037OAI: oai:DiVA.org:liu-54037DiVA: diva2:297932
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
In thesis
1. Signal Processing for Robust and Real-Time fMRI With Application to Brain Computer Interfaces
Open this publication in new window or tab >>Signal Processing for Robust and Real-Time fMRI With Application to Brain Computer Interfaces
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:nbn:se:liu:diva-54040 (URN)LIU-TEK-LIC-2010:3 (Local ID)978-91-7393-431-2 (ISBN)LIU-TEK-LIC-2010:3 (Archive number)LIU-TEK-LIC-2010:3 (OAI)
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

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Eklund, AndersWarntjes, MarcelAndersson, MatsKnutsson, Hans

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