Parallel computing for simultaneous iterative tomographic imaging by graphics processing units

Pedro D. Bello-Maldonado; Ricardo López; Colleen Rogers; Yuanwei Jin; Enyue Lu

doi:10.1117/12.2223466

20 May 2016 Parallel computing for simultaneous iterative tomographic imaging by graphics processing units

Pedro D. Bello-Maldonado, Ricardo López, Colleen Rogers, Yuanwei Jin, Enyue Lu

Proceedings Volume 9870, Computational Imaging; 987009 (2016) https://doi.org/10.1117/12.2223466
Event: SPIE Commercial + Scientific Sensing and Imaging, 2016, Baltimore, MD, United States

Abstract

In this paper, we address the problem of accelerating inversion algorithms for nonlinear acoustic tomographic imaging by parallel computing on graphics processing units (GPUs). Nonlinear inversion algorithms for tomographic imaging often rely on iterative algorithms for solving an inverse problem, thus computationally intensive. We study the simultaneous iterative reconstruction technique (SIRT) for the multiple-input-multiple-output (MIMO) tomography algorithm which enables parallel computations of the grid points as well as the parallel execution of multiple source excitation. Using graphics processing units (GPUs) and the Compute Uniﬁed Device Architecture (CUDA) programming model an overall improvement of 26.33x was achieved when combining both approaches compared with sequential algorithms. Furthermore we propose an adaptive iterative relaxation factor and the use of non-uniform weights to improve the overall convergence of the algorithm. Using these techniques, fast computations can be performed in parallel without the loss of image quality during the reconstruction process.

Citation Download Citation

Pedro D. Bello-Maldonado, Ricardo López, Colleen Rogers, Yuanwei Jin, and Enyue Lu "Parallel computing for simultaneous iterative tomographic imaging by graphics processing units", Proc. SPIE 9870, Computational Imaging, 987009 (20 May 2016); https://doi.org/10.1117/12.2223466

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