Accelerating Image Reconstruction in Three-Dimensional Optoacoustic Tomography on Graphics Processing Units

Kun Wang, Chao Huang, Yu-Jiun Kao, Cheng-Ying Chou, Alexander A. Oraevsky, Mark A. Anastasio
Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63130
Med. Phys. 40, 023301 (2013); arXiv:1304.2017 [physics.med-ph], (7 Apr 2013)


   author={Wang}, K. and {Huang}, C. and {Kao}, Y.-J. and {Chou}, C.-Y. and {Oraevsky}, A.~A. and {Anastasio}, M.~A.},

   title={"{Accelerating image reconstruction in three-dimensional optoacoustic tomography on graphics processing units}"},

   journal={Medical Physics},










   adsnote={Provided by the SAO/NASA Astrophysics Data System}


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PURPOSE: Optoacoustic tomography (OAT) is inherently a three-dimensional (3D) inverse problem. However, most studies of OAT image reconstruction still employ two-dimensional (2D) imaging models. One important reason is because 3D image reconstruction is computationally burdensome. The aim of this work is to accelerate existing image reconstruction algorithms for 3D OAT by use of parallel programming techniques. METHODS: Parallelization strategies are proposed to accelerate a filtered backprojection (FBP) algorithm and two different pairs of projection/backprojection operations that correspond to two different numerical imaging models. The algorithms are designed to fully exploit the parallel computing power of graphic processing units (GPUs). In order to evaluate the parallelization strategies for the projection/backprojection pairs, an iterative image reconstruction algorithm is implemented. Computer-simulation and experimental studies are conducted to investigate the computational efficiency and numerical accuracy of the developed algorithms. RESULTS: The GPU implementations improve the computational efficiency by factors of 1, 000, 125, and 250 for the FBP algorithm and the two pairs of projection/backprojection operators, respectively. Accurate images are reconstructed by use of the FBP and iterative image reconstruction algorithms from both computer-simulated and experimental data. CONCLUSIONS: Parallelization strategies for 3D OAT image reconstruction are proposed for the first time. These GPU-based implementations significantly reduce the computational time for 3D image reconstruction, complementing our earlier work on 3D OAT iterative image reconstruction.
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