Abstract

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 3-D oximetric imaging experiment involving digital band pass filtering and background noise subtraction, followed by 3-D Fourier reconstruction. This process is rather slow in a conventional uni-processor system. This paper presents the design and implementation of 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, a reliable parallel programming paradigm. The code is executed on four Dual-Core AMD Opteron shared memory processors, to reduce the computational burden of the digital 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. The parallel system required 18 seconds for the generation of filtered 3-D data. In addition, a parallel Matlab code has been developed to perform 3-D Fourier reconstruction and executed on the parallel processor. Speedup factors of 4.57 and 4.25 have been achieved during the reconstruction process and oximetry computation, for a data set with 23 x 23 x 23 gradient steps. We report the results of this parallel processing approach from experiments conducted on phantoms and small animals. 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 bio-physical parameters from 3-D EPR oximetric imaging, almost in real-time. Key words: Oximetric EPR imaging, Multi-gradient 3-D oximetry, parallel computing, OpenMP, parallel Matlab, Distributed Computing Tool, FFT-based reconstruction



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