Chinese Optics Letters, Volume. 14, Issue 7, 071701(2016)
GPU accelerated simplified harmonic spherical approximation equations for three-dimensional optical imaging
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Fig. 1. Flowchart of the GPU-accelerated framework for the SPN equations.
Fig. 2. Kernel function of matrix-vector multiplication for the CSR sparse matrix format using 32 thread warp per matrix row.
Fig. 3. Phantoms used in accuracy validation: (a) homogeneous cylindrical phantom. (b) Digital mouse model based phantom. (c) and (d) comparative results between the GPU-accelerated SPN equations (
Fig. 4. (a) Speed-up ratio of the total processing time using the GPU-accelerated SPN method over the CPU-based one. (b) Speed-up ratio of the GPU-accelerated CG solver over the CPU one for solving the system matrix of SP7 equations.
Table 1. Optical Properties of Digital Mouse Phantom at the Wavelength of 650 nm, including Absorption Coefficient (μa), Scattering Coefficient (μs), Anisotropy Coefficient (g), and Refractive Index (n)
View tableView in ArticleTable 1. Optical Properties of Digital Mouse Phantom at the Wavelength of 650 nm, including Absorption Coefficient (μa), Scattering Coefficient (μs), Anisotropy Coefficient (g), and Refractive Index (n)
Tissue ( ) ( ) Muscle 0.11636 4.6735 0.9 1.37 Liver 0.47078 6.999 0.9 1.37 Lung 0.26296 36.818 0.94 1.37 Heart 0.07859 6.7104 0.85 1.37 Kidney 0.08811 16.846 0.86 1.37 Stomach 0.01504 18.497 0.92 1.37
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Shenghan Ren, Xueli Chen, Xu Cao, Shouping Zhu, Jimin Liang, "GPU accelerated simplified harmonic spherical approximation equations for three-dimensional optical imaging," Chin. Opt. Lett. 14, 071701 (2016)
Paper Information
Category: Medical optics and biotechnology
Received: Mar. 3, 2016
Accepted: Apr. 22, 2016
Published Online: Aug. 3, 2018
The Author Email: Jimin Liang (jimleung@mail.xidian.edu.cn)
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