[A Method for Fluorescent Diffuse Optical Tomography Based on Lattice Boltzmann Forward Model on GPU Parallelization].

Fluorescent Diffuse Optical Tomography GPU Lattice Boltzmann method

Journal

Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation
ISSN: 1671-7104
Titre abrégé: Zhongguo Yi Liao Qi Xie Za Zhi
Pays: China
ID NLM: 9426153

Informations de publication

Date de publication:
08 Feb 2020
Historique:
entrez: 14 5 2020
pubmed: 14 5 2020
medline: 25 8 2020
Statut: ppublish

Résumé

Fluorescent Diffuse Optical Tomography (FDOT) is an emerging imaging method with great prospects in fields of biology and medicine. However, the current solutions to the forward problem in FDOT are time consuming, which greatly limit the application. We proposed a method for FDOT based on Lattice Boltzmann forward model on GPU to greatly improve the computational efficiency. The Lattice Boltzmann Method (LBM) was used to construct the optical transmission model. This method separated the LBM into collision, streaming and boundary processing processes on GPUs to perform the LBM efficiently, which were local computational and inefficient on CPU. The feasibility of the proposed method was verified by the numerical phantom and the physical phantom experiments. The experimental results showed that the proposed method achieved the best performance of a 118-fold speed up under the precondition of simulation accuracy, comparing to the diffusion equation implemented by Finite Element Method (FEM) on CPU. Thus, the LBM on the GPU may efficiently solve the forward problem in FDOT.

Identifiants

pubmed: 32400979
doi: 10.3969/j.issn.1671-7104.2020.02.001
doi:

Types de publication

Journal Article

Langues

chi

Sous-ensembles de citation

IM

Pagination

95-100

Auteurs

Huandi Wu (H)

School of Communication and Information Engineering, Shanghai University, Shanghai, 200444.

Zhuangzhi Yan (Z)

School of Communication and Information Engineering, Shanghai University, Shanghai, 200444.

Xingxing Cen (X)

School of Communication and Information Engineering, Shanghai University, Shanghai, 200444.

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Classifications MeSH