Reconstruction for Time‐Domain in vivo EPR 3D Multigradient Oximetric Imaging—A Parallel Processing Perspective

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 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.

Low-Field Magnetic Resonance Imaging to Visualize Chronic and Cycling Hypoxia in Tumor-Bearing Mice

“…Percentage of tumor blood volume was estimated by the expression 100 × (S pre − S post ) / [S pre + S post (W b /W t − 1)], where S pre and S post were the signal intensities before and after USPIO injection and W b and W t were the intra- and extravascular water fractions (35, 42). Co-registration of EPRI and MRI images was accomplished using code written in MATLAB (Mathworks) as described previously (35, 37). …”

Co-imaging of the tumor oxygenation and metabolism using electron paramagnetic resonance imaging and 13-C hyperpolarized magnetic resonance imaging before and after irradiation

“…For convenience of co-registration with EPRI, all MRI images had the same filed of view (FOV) of 32 mm and slice thickness of 2 mm. Co-registration of EPRI and MRI images was accomplished using a code written in MATLAB (Mathworks) script as previously described [ 8 , 18 , 43 ].…”

PSNR Based Optimization Applied to Algebraic Reconstruction Technique for Image Reconstruction on a Multi-Core System

“…In recent years, parallel computing with massive data has emerged as a key technology in imaging techniques also. Shared memory parallelization has been proved to be a best way to attain better runtime performance recently for image reconstruction [15]. A shared-memory multiprocessor (SMP) consists of a number of processors accessing one or more shared memory modules.…”

“…A shared-memory multiprocessor (SMP) consists of a number of processors accessing one or more shared memory modules. The penalty of using inter-processor communication is not up to the mark on SMP compared to distributed memory architectures [15]. For a relatively large data size, it is advantageous to use SMP architecture.…”