GLM Analysis for fMRI using Connex Array
Keywords:
Connex Array, Functional magnetic resonance imaging, Image reconstruction, Parallel algorithms, Parallel processingAbstract
In the last decades, magnetic resonance imaging gained lot of popularity, and also functional magnetic resonance imaging (fMRI), due to the fact that MRI is a harmless and efficient technique for human cerebral activity studies; fMRI aims to determine and to locate different brain activities when the subject is doing a predetermined task. In addition, using fMRI analysis, nowadays we can make prediction on several diseases. This paper’s purpose is to describe the General Linear Model for fMRI statistical analysis algorithm, for a 64 x 64 x 22 voxels dataset on a revolutionary parallel computing machine, Connex Array. We make a  comparison to other computing machines used in the same purpose, in terms of algorithm time execution (statistical analysis speed). We will show that by taking advantage on its specific parallel computation each step in GLM analysis, Connex Array is able to answer successfully to computational challenge launched by fMRI computation: the
speed-up.
References
Tong, S.; Alessio, A.M. (2010); Noise properties in PSF—based fully—3D PET image reconstruction: an experimental evaluation, Physics in Medicine and Biology, 55: 1453—1473. http://dx.doi.org/10.1088/0031-9155/55/5/013
Chen, C.M.; Lee, S.Y. (1990); A parallel implementation of 3—D CT Image reconstruction on hypercube multiprocessor, IEEE Transactions on Nuclear Science, 37(3): 1333-1346. http://dx.doi.org/10.1109/23.57385
Nishimoto, S.; Vu, A.T.; Naselaris, Th.; Benjamini, Y.; Yu, B.; Gallant, J.L. (2011); Reconstructing Visual Experiences from Brain Activity Evoked by Natural Movies, Current Biology 21, 1641—1646.
Holland, D.; Liu, J.; Song, C.; Mazerolle, X. et al. (2013); Compressed sensing reconstruction improves sensitivity of variable density spiral fMRI, Magnetic Resonance in Medicine, 70.
Lindquist, M.A. (2008); The Statistical Analysis of fMRI Data, Statistical Science, 23(4): 439—464. http://dx.doi.org/10.1214/09-STS282
Cohen, M.S. (2001); Real—Time Functional Magnetic Resonance Imaging, Methods, 25(2): 201—220. http://dx.doi.org/10.1006/meth.2001.1235
Bernstein, M.A.; King, K.F.; Zhou, X.J. (2004); Handbook of MRI Pulse Sequences, Elsevier Academic Press.
http://v04.pymvpa.org/examples.html
Maliţa, M.; Åžtefan, Gh. M. (2010); Many-processors & KLEENE's model, UPB Scientific Bulletin Series C, 72.
Ştefan, Gh. M. (2010); Integral Parallel Architecture In System—On—Chip Designs, The 6th International Workshop on Unique Chips and Systems, Atlanta, USA, 23—26. http://www.arh.pub.ro/gstefan/2010ucas.pdf
Mîţu, B. (2008); C Language Extension for Parallel Processing. http://arh.pub.ro/gstefan/VectorC.ppt
Cooley, J.W.; Tuckey, J.W. (1965); An Algorithm for the Machine Calculation of Complex Fourier Series. Math. Computation, JSTOR Mathematic of Computation, 19(90):297-309.
Å¢ugui, A. (2012); FFT Parallel Implementation for MRI Image Reconstruction, U.P.B. Scientific Bulletin Series C, 74: 229-244.
Eklund, A.; Anderson, M.; Knutsson, H. (2012); fMRI Analysis on the GPU Possibilities and Challenges, Computer Methods and Programs in Biomedicine, 145—161. http://dx.doi.org/10.1016/j.cmpb.2011.07.007
Ţugui, A. (2013); Fixed—point real time MRI reconstruction using Connex Array, Proceedings of the Romanian Academy Series A, 14(3): 255—258. http://www.acad.ro/sectii2002/proceedings/doc2013-3/11-Tugui.pdf
Eklund, A.; Andresson, M.; Knutsson, H. (2010); Phase based volume registration using CUDA, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Dallas, USA, 658—661.
Calfa, A.M.; Ştefan, Gh. M. (2010); Matrix Computation on Connex Parallel Architecture, ICES 2010 —The International Conference on Signals and Electronic Systems, Gliwice, Poland, 375—378.
Friman, O.; Borga, M.; Lundberg, P.; Knutsson, H. (2004); Detection and detrending in fMRI data analysis, NeuroImage, 22(2): 645—655. http://dx.doi.org/10.1016/j.neuroimage.2004.01.033
Tanabe, J.; Miller, D.; Tregellas, J.; Freedman, R.; Meyer, F.G. (2002); Comparison of Detrending Methods for Optimal fMRI Preprocessing, NeuroImage, 15(4): 902—907. http://dx.doi.org/10.1006/nimg.2002.1053
Poldrack, R.H.; Mumford, J.A. (2011); Handbook of Functional MRI Data Analysis, Cambridge University Press, New York, USA.
Published
Issue
Section
License
ONLINE OPEN ACCES: Acces to full text of each article and each issue are allowed for free in respect of Attribution-NonCommercial 4.0 International (CC BY-NC 4.0.
You are free to:
-Share: copy and redistribute the material in any medium or format;
-Adapt: remix, transform, and build upon the material.
The licensor cannot revoke these freedoms as long as you follow the license terms.
DISCLAIMER: The author(s) of each article appearing in International Journal of Computers Communications & Control is/are solely responsible for the content thereof; the publication of an article shall not constitute or be deemed to constitute any representation by the Editors or Agora University Press that the data presented therein are original, correct or sufficient to support the conclusions reached or that the experiment design or methodology is adequate.
