Name
Abstract | ||
|---|---|---|
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 similar to 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 x 1 mm(2) to 2 x 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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.3390/s19184000 | SENSORS |
Keywords | Field | DocType |
parallel computing,photoacoustic microscopy,blood flow,correlation analysis,GPU | MATLAB,Blood flow,Medical imaging,Parallel computing,Ranging,Engineering,Microscopy,Graphics processing unit,Computation,Speedup | Journal |
Volume | Issue | ISSN |
19 | 18 | 1424-8220 |
Citations | PageRank | References |
1 | 0.39 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhiqiang Xu | 1 | 1 | 0.39 |
| Yiming Wang | 2 | 2 | 1.44 |
| Naidi Sun | 3 | 4 | 0.87 |
| Zhengying Li | 4 | 2 | 2.12 |
| Song Hu | 5 | 5 | 1.68 |
| Quan Liu | 6 | 16 | 2.94 |