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Proceedings Paper

Graphics processing units accelerated MIMO tomographic image reconstruction using target sparseness
Author(s): Pedro D. Bello-Maldonado; Agustin Rivera-Longoria; Mark Idleman; Yuanwei Jin; Enyue Lu
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Paper Abstract

GPU computing of medical imaging applications adds an extra layer of acceleration after mathematical algorithms are used to reduce computation times. Our work improves the performance of the multiple-input multiple-output ultrasonic tomography algorithm, by using target sparseness and GPUs with CUDA. The main goal was to determine how GPUs can be best used to accelerate sparsity-aware algorithms for ultrasonic tomography applications. We present smart kernels to compute portions of the algorithm that exploit GPU resources such as shared memory and computing units that can be applied to other applications. Using our accelerated algorithm, we analyze different sparsity constraints setups and evaluate how GPU ultrasonic tomography with target sparseness behaves against the same algorithm that does not incorporate prior knowledge of target sparseness.

Paper Details

Date Published: 23 May 2014
PDF: 12 pages
Proc. SPIE 9109, Compressive Sensing III, 91090O (23 May 2014); doi: 10.1117/12.2050355
Show Author Affiliations
Pedro D. Bello-Maldonado, Florida International Univ. (United States)
Agustin Rivera-Longoria, Texas State Univ. (United States)
Mark Idleman, Ahmerst College (United States)
Yuanwei Jin, Univ. of Maryland Eastern Shore (United States)
Enyue Lu, Salisbury Univ. (United States)


Published in SPIE Proceedings Vol. 9109:
Compressive Sensing III
Fauzia Ahmad, Editor(s)

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