一种基于格子玻尔兹曼前向模型的GPU并行加速荧光扩散断层成像的方法

荧光扩散断层成像是一种新兴的成像方式,在生物学和医学等领域具有广阔的应用前景。而目前荧光扩散断层成像中前向问题的求解耗时严重,该问题大大限制应用场景。为了提高荧光断层成像的计算速度,该研究团队提出了一种基于格子玻尔兹曼前向模型的GPU并行加速荧光扩散断层成像的方法。该研究利用格子玻尔兹曼方法构建光传输模型,把在CPU上计算效率低的LBM的碰撞、迁移、边界处理过程在GPU上加速处理,从而加速荧光扩散断层成像的求解。并采用仿真实验和仿体实验验证该方法的可行性。实验结果表明了在和传统求解的扩散方程计算结果一致的前提下,所提方法相比于在CPU上用基于扩散方程有限元方法能达到最高118倍的加速比。因此,结合GPU的LBM方法可有效求解FDOT中的前向问题。

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

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.