Open AccessGPU-based acceleration and mesh optimization of finite-element-method-based quantitative photoacoustic tomography: a step towards clinical applications
Author(s) -
Tianqi Shan,
Jun Qi,
Meilin Jiang,
Huabei Jiang
Publication year - 2017
Publication title -
applied optics
Language(s) - English
Resource type - Journals
ISSN - 0003-6935
DOI - 10.1364/ao.56.004426
Subject(s) - computer science , finite element method , imaging phantom , iterative reconstruction , tomography , acceleration , graphics processing unit , computational science , image quality , reconstruction algorithm , adaptive mesh refinement , computation , mesh generation , image processing , algorithm , computer vision , optics , image (mathematics) , parallel computing , physics , classical mechanics , thermodynamics
Finite element method (FEM)-based time-domain quantitative photoacoustic tomography (TD-qPAT) is a powerful approach, as it provides highly accurate quantitative imaging capability by recovering absolute tissue absorption coefficients for functional imaging. However, this approach is extremely computationally demanding, and requires days for the reconstruction of one set of images, making it impractical to be used in clinical applications, where a large amount of data needs to be processed in a limited time scale. To address this challenge, here we present a graphic processing unit (GPU)-based parallelization method to accelerate the image reconstruction using FEM-based TD-qPAT. In addition, to further optimize FEM-based TD-qPAT reconstruction, an adaptive meshing technique, along with mesh density optimization, is adopted. Phantom experimental data are used in our study to evaluate the GPU-based TD-qPAT algorithm, as well as the adaptive meshing technique. The results show that our new approach can considerably reduce the computation time by at least 136-fold over the current central processing unit (CPU)-based algorithm. The quality of image reconstruction is also improved significantly when adaptive meshing and mesh density optimization are applied.