Fluorescent diffuse optical tomography (FDOT) is an emerging imaging modality, with great prospects in areas such as biology and medicine. However, current FDOT encounters difficulty in simulating photon propagation in biological tissue, i.e., the forward problem, which limits its further application in biomedical research. This paper presents a lattice Boltzmann method (LBM) on the GPU to greatly improve the computational efficiency in the forward problem realization. This method separated the LBM simulating the propagation of photon in tissues into collision, streaming and boundary processing processes on GPUs, which are local computational processes and inefficient on CPU, so that we can perform the LBM efficiently. Both the numerical phantom and the physical phantom experiments were carried out to evaluate the performance of the proposed method. The experimental results showed that the proposed method achieved the best performance of 2471 mega lattice-updates per second (MLUPS) and a 118-fold speedup under the precondition of simulation accuracy, compared to the diffusion equation implemented by finite element method (FEM) on CPU. Thus, the LBM on the GPU has the potential for efficiently solving the forward problem in FDOT.

荧光扩散光学层析成像（FDOT）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前景。然而，当前的FDOT在模拟光子在生物组织中的传播方面遇到困难，即正向问题，这限制了其在生物医学研究中的进一步应用。本文提出了一种基于GPU的格子Boltzmann方法（LBM），可以大大提高正向问题实现中的计算效率。该方法将模拟组织中光子传播的LBM分为GPU上的碰撞，流传输和边界处理过程，这些过程是局部计算过程，在CPU上效率低下，因此我们可以高效地执行LBM。数值体模和物理体模实验都进行了评估该方法的性能。实验结果表明，与有限元方法（FEM）实现的扩散方程相比，该方法在模拟精度前提下，具有每秒2471兆格更新（MLUPS）和118倍加速的最佳性能。中央处理器。因此，GPU上的LBM具有有效解决FDOT中的前向问题的潜力。