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True 4D Image Denoising on the GPU
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.
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
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. Vol. 2011
Keyword [en]
Image denoising, Graphics processing unit (GPU), 4D, Computed tomography (CT)
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-69678DOI: 10.1155/2011/952819OAI: oai:DiVA.org:liu-69678DiVA: diva2:430974
Available from: 2011-07-13 Created: 2011-07-13 Last updated: 2017-12-08
In thesis
1. Computational Medical Image Analysis: With a Focus on Real-Time fMRI and Non-Parametric Statistics
Open this publication in new window or tab >>Computational Medical Image Analysis: With a Focus on Real-Time fMRI and Non-Parametric Statistics
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
functional magnetic resonance imaging, brain computer interfaces, canonical correlation analysis, random permutation test, graphics processing unit
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-76120 (URN)978-91-7519-921-4 (ISBN)
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

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