Reconstruction for Time-Domain In Vivo EPR 3D Multigradient Oximetric Imaging—A Parallel Processing Perspective
Three-dimensional Oximetric Electron Paramagnetic Resonance Imaging using the Single Point Imaging modality generates unpaired spin density and oxygen images that can readily distinguish between normal and tumor tissues in small animals. It is also possible with fast imaging to track the changes in...
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| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2009-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2009/528639 |
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| author | Christopher D. Dharmaraj Kishan Thadikonda Anthony R. Fletcher Phuc N. Doan Nallathamby Devasahayam Shingo Matsumoto Calvin A. Johnson John A. Cook James B. Mitchell Sankaran Subramanian Murali C. Krishna |
| author_facet | Christopher D. Dharmaraj Kishan Thadikonda Anthony R. Fletcher Phuc N. Doan Nallathamby Devasahayam Shingo Matsumoto Calvin A. Johnson John A. Cook James B. Mitchell Sankaran Subramanian Murali C. Krishna |
| author_sort | Christopher D. Dharmaraj |
| collection | DOAJ |
| description | Three-dimensional Oximetric Electron Paramagnetic Resonance Imaging using the Single Point Imaging modality generates unpaired spin density and oxygen images that can readily distinguish between normal and tumor tissues in small animals. It is also possible with fast imaging to track the changes in tissue oxygenation in response to the oxygen content in the breathing air. However, this involves dealing with gigabytes of data for each 3D oximetric imaging experiment involving digital band pass filtering and background noise subtraction, followed by 3D Fourier reconstruction. This process is rather slow in a conventional uniprocessor system. This paper presents a parallelization framework using OpenMP runtime support and parallel MATLAB to execute such computationally intensive programs. The Intel compiler is used to develop a parallel C++ code based on OpenMP. The code is executed on four Dual-Core AMD Opteron shared memory processors, to reduce the computational burden of the filtration task significantly. The results show that the parallel code for filtration has achieved a speed up factor of 46.66 as against the equivalent serial MATLAB code. In addition, a parallel MATLAB code has been developed to perform 3D Fourier reconstruction. Speedup factors of 4.57 and 4.25 have been achieved during the reconstruction process and oximetry computation, for a data set with 23×23×23 gradient steps. The execution time has been computed for both the serial and parallel implementations using different dimensions of the data and presented for comparison. The reported system has been designed to be easily accessible even from low-cost personal computers through local internet (NIHnet). The experimental results demonstrate that the parallel computing provides a source of high computational power to obtain biophysical parameters from 3D EPR oximetric imaging, almost in real-time. |
| format | Article |
| id | doaj-art-6aa6bd87577e4a9db98cf7b485ba4bc9 |
| institution | Kabale University |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2009-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-6aa6bd87577e4a9db98cf7b485ba4bc92025-02-03T06:44:22ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962009-01-01200910.1155/2009/528639528639Reconstruction for Time-Domain In Vivo EPR 3D Multigradient Oximetric Imaging—A Parallel Processing PerspectiveChristopher D. Dharmaraj0Kishan Thadikonda1Anthony R. Fletcher2Phuc N. Doan3Nallathamby Devasahayam4Shingo Matsumoto5Calvin A. Johnson6John A. Cook7James B. Mitchell8Sankaran Subramanian9Murali C. Krishna10Radiation Biology Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892-1002, USARadiation Biology Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892-1002, USACenter for Information Technology, NIH, Bethesda, MD 20892, USACenter for Information Technology, NIH, Bethesda, MD 20892, USARadiation Biology Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892-1002, USARadiation Biology Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892-1002, USACenter for Information Technology, NIH, Bethesda, MD 20892, USARadiation Biology Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892-1002, USARadiation Biology Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892-1002, USARadiation Biology Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892-1002, USARadiation Biology Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892-1002, USAThree-dimensional Oximetric Electron Paramagnetic Resonance Imaging using the Single Point Imaging modality generates unpaired spin density and oxygen images that can readily distinguish between normal and tumor tissues in small animals. It is also possible with fast imaging to track the changes in tissue oxygenation in response to the oxygen content in the breathing air. However, this involves dealing with gigabytes of data for each 3D oximetric imaging experiment involving digital band pass filtering and background noise subtraction, followed by 3D Fourier reconstruction. This process is rather slow in a conventional uniprocessor system. This paper presents a parallelization framework using OpenMP runtime support and parallel MATLAB to execute such computationally intensive programs. The Intel compiler is used to develop a parallel C++ code based on OpenMP. The code is executed on four Dual-Core AMD Opteron shared memory processors, to reduce the computational burden of the filtration task significantly. The results show that the parallel code for filtration has achieved a speed up factor of 46.66 as against the equivalent serial MATLAB code. In addition, a parallel MATLAB code has been developed to perform 3D Fourier reconstruction. Speedup factors of 4.57 and 4.25 have been achieved during the reconstruction process and oximetry computation, for a data set with 23×23×23 gradient steps. The execution time has been computed for both the serial and parallel implementations using different dimensions of the data and presented for comparison. The reported system has been designed to be easily accessible even from low-cost personal computers through local internet (NIHnet). The experimental results demonstrate that the parallel computing provides a source of high computational power to obtain biophysical parameters from 3D EPR oximetric imaging, almost in real-time.http://dx.doi.org/10.1155/2009/528639 |
| spellingShingle | Christopher D. Dharmaraj Kishan Thadikonda Anthony R. Fletcher Phuc N. Doan Nallathamby Devasahayam Shingo Matsumoto Calvin A. Johnson John A. Cook James B. Mitchell Sankaran Subramanian Murali C. Krishna Reconstruction for Time-Domain In Vivo EPR 3D Multigradient Oximetric Imaging—A Parallel Processing Perspective International Journal of Biomedical Imaging |
| title | Reconstruction for Time-Domain In Vivo EPR 3D Multigradient Oximetric Imaging—A Parallel Processing Perspective |
| title_full | Reconstruction for Time-Domain In Vivo EPR 3D Multigradient Oximetric Imaging—A Parallel Processing Perspective |
| title_fullStr | Reconstruction for Time-Domain In Vivo EPR 3D Multigradient Oximetric Imaging—A Parallel Processing Perspective |
| title_full_unstemmed | Reconstruction for Time-Domain In Vivo EPR 3D Multigradient Oximetric Imaging—A Parallel Processing Perspective |
| title_short | Reconstruction for Time-Domain In Vivo EPR 3D Multigradient Oximetric Imaging—A Parallel Processing Perspective |
| title_sort | reconstruction for time domain in vivo epr 3d multigradient oximetric imaging a parallel processing perspective |
| url | http://dx.doi.org/10.1155/2009/528639 |
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