Parallel Computing for Quantitative Blood Flow Imaging in Photoacoustic Microscopy

Photoacoustic microscopy (PAM) is an emerging biomedical imaging technology capable of quantitative measurement of the microvascular blood flow by correlation analysis. However, the computational cost is high, limiting its applications. Here, we report a parallel computation design based on graphics...

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Autores principales: Xu, Zhiqiang, Wang, Yiming, Sun, Naidi, Li, Zhengying, Hu, Song, Liu, Quan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6767147/
https://www.ncbi.nlm.nih.gov/pubmed/31527505
http://dx.doi.org/10.3390/s19184000
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author Xu, Zhiqiang
Wang, Yiming
Sun, Naidi
Li, Zhengying
Hu, Song
Liu, Quan
author_facet Xu, Zhiqiang
Wang, Yiming
Sun, Naidi
Li, Zhengying
Hu, Song
Liu, Quan
author_sort Xu, Zhiqiang
collection PubMed
description Photoacoustic microscopy (PAM) is an emerging biomedical imaging technology capable of quantitative measurement of the microvascular blood flow by correlation analysis. However, the computational cost is high, limiting its applications. Here, we report a parallel computation design based on graphics processing unit (GPU) for high-speed quantification of blood flow in PAM. Two strategies were utilized to improve the computational efficiency. First, the correlation method in the algorithm was optimized to avoid redundant computation and a parallel computing structure was designed. Second, the parallel design was realized on GPU and optimized by maximizing the utilization of computing resource in GPU. The detailed timings and speedup for each calculation step were given and the MATLAB and C/C++ code versions based on CPU were presented as a comparison. Full performance test shows that a stable speedup of ~80-fold could be achieved with the same calculation accuracy and the computation time could be reduced from minutes to just several seconds with the imaging size ranging from 1 × 1 mm(2) to 2 × 2 mm(2). Our design accelerates PAM-based blood flow measurement and paves the way for real-time PAM imaging and processing by significantly improving the computational efficiency.
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spelling pubmed-67671472019-10-02 Parallel Computing for Quantitative Blood Flow Imaging in Photoacoustic Microscopy Xu, Zhiqiang Wang, Yiming Sun, Naidi Li, Zhengying Hu, Song Liu, Quan Sensors (Basel) Article Photoacoustic microscopy (PAM) is an emerging biomedical imaging technology capable of quantitative measurement of the microvascular blood flow by correlation analysis. However, the computational cost is high, limiting its applications. Here, we report a parallel computation design based on graphics processing unit (GPU) for high-speed quantification of blood flow in PAM. Two strategies were utilized to improve the computational efficiency. First, the correlation method in the algorithm was optimized to avoid redundant computation and a parallel computing structure was designed. Second, the parallel design was realized on GPU and optimized by maximizing the utilization of computing resource in GPU. The detailed timings and speedup for each calculation step were given and the MATLAB and C/C++ code versions based on CPU were presented as a comparison. Full performance test shows that a stable speedup of ~80-fold could be achieved with the same calculation accuracy and the computation time could be reduced from minutes to just several seconds with the imaging size ranging from 1 × 1 mm(2) to 2 × 2 mm(2). Our design accelerates PAM-based blood flow measurement and paves the way for real-time PAM imaging and processing by significantly improving the computational efficiency. MDPI 2019-09-16 /pmc/articles/PMC6767147/ /pubmed/31527505 http://dx.doi.org/10.3390/s19184000 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Xu, Zhiqiang
Wang, Yiming
Sun, Naidi
Li, Zhengying
Hu, Song
Liu, Quan
Parallel Computing for Quantitative Blood Flow Imaging in Photoacoustic Microscopy
title Parallel Computing for Quantitative Blood Flow Imaging in Photoacoustic Microscopy
title_full Parallel Computing for Quantitative Blood Flow Imaging in Photoacoustic Microscopy
title_fullStr Parallel Computing for Quantitative Blood Flow Imaging in Photoacoustic Microscopy
title_full_unstemmed Parallel Computing for Quantitative Blood Flow Imaging in Photoacoustic Microscopy
title_short Parallel Computing for Quantitative Blood Flow Imaging in Photoacoustic Microscopy
title_sort parallel computing for quantitative blood flow imaging in photoacoustic microscopy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6767147/
https://www.ncbi.nlm.nih.gov/pubmed/31527505
http://dx.doi.org/10.3390/s19184000
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