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Computational Medical Image Analysis: With a Focus on Real-Time fMRI and Non-Parametric Statistics
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Functional magnetic resonance imaging (fMRI) is a prime example of multi-disciplinary research. Without the beautiful physics of MRI, there wouldnot be any images to look at in the first place. To obtain images of goodquality, it is necessary to fully understand the concepts of the frequencydomain. The analysis of fMRI data requires understanding of signal pro-cessing, statistics and knowledge about the anatomy and function of thehuman brain. The resulting brain activity maps are used by physicians,neurologists, psychologists and behaviourists, in order to plan surgery andto increase their understanding of how the brain works.

This thesis presents methods for real-time fMRI and non-parametric fMRIanalysis. Real-time fMRI places high demands on the signal processing,as all the calculations have to be made in real-time in complex situations.Real-time fMRI can, for example, be used for interactive brain mapping.Another possibility is to change the stimulus that is given to the subject, inreal-time, such that the brain and the computer can work together to solvea given task, yielding a brain computer interface (BCI). Non-parametricfMRI analysis, for example, concerns the problem of calculating signifi-cance thresholds and p-values for test statistics without a parametric nulldistribution.

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

A graphics processing unit (GPU) implementation of a random permuta-tion test for single subject fMRI analysis is also presented. The randompermutation test is used to calculate significance thresholds and p-values forfMRI analysis by canonical correlation analysis (CCA), and to investigatethe correctness of standard parametric approaches. The random permuta-tion test was verified by using 10 000 noise datasets and 1484 resting statefMRI datasets. The random permutation test is also used for a non-localCCA approach to fMRI analysis.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2012. , 119 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1439
Keyword [en]
functional magnetic resonance imaging, brain computer interfaces, canonical correlation analysis, random permutation test, graphics processing unit
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-76120ISBN: 978-91-7519-921-4 (print)OAI: oai:DiVA.org:liu-76120DiVA: diva2:512491
Public defence
2012-04-27, Eken, Campus US, Linköping University, Linköping, 09:00 (English)
Opponent
Supervisors
Available from: 2012-03-28 Created: 2012-03-28 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. 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
3. Using the Local Phase of the Magnitude of the Local Structure Tensor for Image Registration
Open this publication in new window or tab >>Using the Local Phase of the Magnitude of the Local Structure Tensor for Image Registration
2011 (English)In: Image Analysis: 17th Scandinavian Conference, SCIA 2011, Ystad, Sweden, May 2011. Proceedings / [ed] Anders Heyden, Fredrik Kahl, Springer Berlin/Heidelberg, 2011, Vol. 6688, 414-423 p.Conference paper, Published paper (Refereed)
Abstract [en]

The need of image registration is increasing, especially in the medical image domain. The simplest kind of image registration is to match two images that have similar intensity. More advanced cases include the problem of registering images of different intensity, for which phase based algorithms have proven to be superior. In some cases the phase based registration will fail as well, for instance when the images to be registered do not only differ in intensity but also in local phase. This is the case if a dark circle in the reference image is a bright circle in the source image. While rigid registration algorithms can use other parts of the image to calculate the global transformation, this problem is harder to solve for non-rigid registration. The solution that we propose in this work is to use the local phase of the magnitude of the local structure tensor, instead of the local phase of the image intensity. By doing this, we achieve invariance both to the image intensity and to the local phase and thereby only use the structural information, i.e. the shapes of the objects, for registration.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2011
Series
Lecture Notes in Computer Science, ISSN 0302-9743 (print), 1611-3349 (online) ; 6688/2011
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-69246 (URN)10.1007/978-3-642-21227-7_39 (DOI)000308543900039 ()978-3-642-21226-0 (ISBN)
Conference
Image Analysis 17th Scandinavian Conference, SCIA 2011, Ystad, Sweden, May 2011.
Funder
Swedish Research Council, 2007-4786
Note

The original publication is available at www.springerlink.com: Anders Eklund, Daniel Forsberg, Mats Andersson and Hans Knutsson, Using the Local Phase of the Magnitude of the Local Structure Tensor for Image Registration, 2011, Lecture Notes in Computer Science, (6688), 414-432. http://dx.doi.org/10.1007/978-3-642-21227-7_39 Copyright: Springer-verlag http://www.springerlink.com/

Available from: 2011-06-20 Created: 2011-06-20 Last updated: 2015-10-08Bibliographically approved
4. True 4D Image Denoising on the GPU
Open this publication in new window or tab >>True 4D Image Denoising on the GPU
2011 (English)In: International Journal of Biomedical Imaging, ISSN 1687-4188, E-ISSN 1687-4196, Vol. 2011Article in journal (Refereed) Published
Abstract [en]

The use of image denoising techniques is an important part of many medical imaging applications. One common application isto improve the image quality of low-dose, i.e. noisy, computed tomography (CT) data. The medical imaging domain has seen atremendous development during the last decades. It is now possible to collect time resolved volumes, i.e. 4D data, with a number ofmodalities (e.g. ultrasound (US), CT, magnetic resonance imaging (MRI)). While 3D image denoising previously has been appliedto several volumes independently, there has not been much work done on true 4D image denoising, where the algorithm considersseveral volumes at the same time (and not a single volume at a time). By using all the dimensions, it is for example possibleto remove some of the time varying reconstruction artefacts that exist in CT volumes. The problem with 4D image denoising,compared to 2D and 3D denoising, is that the computational complexity increases exponentially.In this paper we describe a novel algorithm for true 4D image denoising, based on local adaptive filtering, and how to implementit on the graphics processing unit (GPU). The algorithm was applied to a 4D CT heart dataset of the resolution 512 x 512 x 445 x 20.The result is that the GPU can complete the denoising in about 25 minutes if spatial filtering is used and in about 8 minutes if FFTbased filtering is used. The CPU implementation requires several days of processing time for spatial filtering and about 50 minutesfor FFT based filtering. Fast spatial filtering makes it possible to apply the denoising algorithm to larger datasets (compared to ifFFT based filtering is used). The short processing time increases the clinical value of true 4D image denoising significantly.

Place, publisher, year, edition, pages
Hindawi Publishing Corporation, 2011
Keyword
Image denoising, Graphics processing unit (GPU), 4D, Computed tomography (CT)
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-69678 (URN)10.1155/2011/952819 (DOI)
Available from: 2011-07-13 Created: 2011-07-13 Last updated: 2017-12-08
5. fMRI Analysis on the GPU - Possibilities and Challenges
Open this publication in new window or tab >>fMRI Analysis on the GPU - Possibilities and Challenges
2012 (English)In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 105, no 2, 145-161 p.Article in journal (Refereed) Published
Abstract [en]

Functional magnetic resonance imaging (fMRI) makes it possible to non-invasively measure brain activity with high spatial resolution.There are however a number of issues that have to be addressed. One is the large amount of spatio-temporal data that needsto be processed. In addition to the statistical analysis itself, several preprocessing steps, such as slice timing correction and motioncompensation, are normally applied. The high computational power of modern graphic cards has already successfully been used forMRI and fMRI. Going beyond the first published demonstration of GPU-based analysis of fMRI data, all the preprocessing stepsand two statistical approaches, the general linear model (GLM) and canonical correlation analysis (CCA), have been implementedon a GPU. For an fMRI dataset of typical size (80 volumes with 64 x 64 x 22 voxels), all the preprocessing takes about 0.5 s on theGPU, compared to 5 s with an optimized CPU implementation and 120 s with the commonly used statistical parametric mapping(SPM) software. A random permutation test with 10 000 permutations, with smoothing in each permutation, takes about 50 s ifthree GPUs are used, compared to 0.5 - 2.5 h with an optimized CPU implementation. The presented work will save time forresearchers and clinicians in their daily work and enables the use of more advanced analysis, such as non-parametric statistics, bothfor conventional fMRI and for real-time fMRI.

Place, publisher, year, edition, pages
Elsevier, 2012
Keyword
Functional magnetic resonance imaging (fMRI), Graphics processing unit (GPU), CUDA, General linear model (GLM), Canonical correlation analysis (CCA), Random permutation test
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-69677 (URN)10.1016/j.cmpb.2011.07.007 (DOI)000300813600005 ()
Note

funding agencies|strategic research center MOVIII||Swedish foundation for strategic research (SSF)||Linnaeus center CADICS||Swedish research council||Linkoping University||

Available from: 2011-07-13 Created: 2011-07-13 Last updated: 2017-12-08Bibliographically approved
6. Fast Random Permutation Tests Enable Objective Evaluation of Methods for Single Subject fMRI Analysis
Open this publication in new window or tab >>Fast Random Permutation Tests Enable Objective Evaluation of Methods for Single Subject fMRI Analysis
2011 (English)In: International Journal of Biomedical Imaging, ISSN 1687-4188, E-ISSN 1687-4196Article in journal (Refereed) Published
Abstract [en]

Parametric statistical methods, such as Z-, t-, and F-values are traditionally employed in functional magnetic resonance imaging (fMRI) for identifying areas in the brain that are active with a certain degree of statistical significance. These parametric methods, however, have two major drawbacks. First, it is assumed that the observed data are Gaussian distributed and independent; assumptions that generally are not valid for fMRI data. Second, the statistical test distribution can be derived theoretically only for very simple linear detection statistics. With non-parametric statistical methods, the two limitations described above can be overcome. The major drawback of non-parametric methods is the computational burden with processing times ranging from hours to days, which so far have made them impractical for routine use in single subject fMRI analysis. In this work, it is shown how the computational power of cost-efficient Graphics Processing Units (GPUs) can be used to speed up random permutation tests. A test with 10 000 permutations takes less than a minute, making statistical analysis of advanced detection methods in fMRI practically feasible. To exemplify the permutation based approach, brain activity maps generated by the General Linear Model (GLM) and Canonical Correlation Analysis (CCA) are compared at the same significance level. During the development of the routines and writing of the paper, 3-4 years of processing time has been saved by using the GPU.

Place, publisher, year, edition, pages
Hindawi Publishing Corporation, 2011
Keyword
Functional magnetic resonance imaging (fMRI), Graphics processing unit (GPU), Non-parametric statistics, random permutation test, CUDA, General Linear Model (GLM), Canonical Correlation Analysis (CCA)
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-69680 (URN)10.1155/2011/627947 (DOI)
Available from: 2011-07-14 Created: 2011-07-14 Last updated: 2017-12-08
7. Does Parametric fMRI Analysis with SPM Yield Valid Results? - An Empirical Study of 1484 Rest Datasets
Open this publication in new window or tab >>Does Parametric fMRI Analysis with SPM Yield Valid Results? - An Empirical Study of 1484 Rest Datasets
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2012 (English)In: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 61, no 3, 565-578 p.Article in journal (Refereed) Published
Abstract [en]

The validity of parametric functional magnetic resonance imaging (fMRI) analysis has only been reported for simulated data.Recent advances in computer science and data sharing make it possible to analyze large amounts of real fMRI data. In this study,1484 rest datasets have been analyzed in SPM8, to estimate true familywise error rates. For a familywise significance threshold of5%, significant activity was found in 1% - 70% of the 1484 rest datasets, depending on repetition time, paradigm and parametersettings. This means that parametric significance thresholds in SPM both can be conservative or very liberal. The main reason forthe high familywise error rates seems to be that the global AR(1) auto correlation correction in SPM fails to model the spectra ofthe residuals, especially for short repetition times. The findings that are reported in this study cannot be generalized to parametricfMRI analysis in general, other software packages may give different results. By using the computational power of the graphicsprocessing unit (GPU), the 1484 rest datasets were also analyzed with a random permutation test. Significant activity was thenfound in 1% - 19% of the datasets. These findings speak to the need for a better model of temporal correlations in fMRI timeseries.

Place, publisher, year, edition, pages
Elsevier, 2012
Keyword
Functional magnetic resonance imaging (fMRI), Familywise error rate, Random field theory, Non-parametric statistics, Random permutation test, Graphics processing unit (GPU)
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-76118 (URN)10.1016/j.neuroimage.2012.03.093 (DOI)000304729800006 ()22507229 (PubMedID)
Note

funding agencies|Linnaeus Center CADICS||Swedish Research Council||Neuroeconomic research group at Linkoping University||GPU hardware||

Available from: 2012-03-28 Created: 2012-03-28 Last updated: 2017-12-07Bibliographically approved
8. A Functional Connectivity Inspired Approach to Non-Local fMRI Analysis
Open this publication in new window or tab >>A Functional Connectivity Inspired Approach to Non-Local fMRI Analysis
2012 (English)In: Proceedings of the 19th IEEE International Conference on Image Processing (ICIP), 2012, IEEE conference proceedings, 2012, 1245-1248 p.Conference paper, Published paper (Other academic)
Abstract [en]

We propose non-local analysis of functional magnetic resonanceimaging (fMRI) data in order to detect more brain activity.Our non-local approach combines the ideas of regularfMRI analysis with those of functional connectivity analysis,and was inspired by the non-local means algorithm thatcommonly is used for image denoising. We extend canonicalcorrelation analysis (CCA) based fMRI analysis to handlemore than one activity area, such that information fromdifferent parts of the brain can be combined. Our non-localapproach is compared to fMRI analysis by the general linearmodel (GLM) and local CCA, by using simulated as well asreal data.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2012
Series
Image Processing, ISSN 1522-4880 ; 2012
Keyword
fMRI, non-local, CCA, functional connectivity, GPU
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-76119 (URN)10.1109/ICIP.2012.6467092 (DOI)978-1-4673-2532-5 (ISBN)978-1-4673-2534-9 (ISBN)
Conference
19th IEEE International Conference on Image Processing (ICIP), 2012, Sept. 30 2012-Oct. 3, Orlando, FL, USA
Available from: 2012-03-28 Created: 2012-03-28 Last updated: 2013-08-28Bibliographically approved

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