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Fast linear solver for radiative transport equation with multiple right hand sides in diffuse optical tomography

机译:扩散光学层析成像中具有多个右侧辐射传递方程的快速线性求解器

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摘要

It is well known that radiative transfer equation (RTE) provides more accurate tomographic results than its diffusion approximation (DA). However, RTE-based tomographic reconstruction codes have limited applicability in practice due to their high computational cost. In this article, we propose a new efficient method for solving the RTE forward problem with multiple light sources in an all-at-once manner instead of solving it for each source separately. To this end, we introduce here a novel linear solver called block biconjugate gradient stabilized method (block BiCGStab) that makes full use of the shared information between different right hand sides to accelerate solution convergence. Two parallelized block BiCGStab methods are proposed for additional acceleration under limited threads situation. We evaluate the performance of this algorithm with numerical simulation studies involving the Delta-Eddington approximation to the scattering phase function. The results show that the single threading block RTE solver proposed here reduces computation time by a factor of 1.5~3 as compared to the traditional sequential solution method and the parallel block solver by a factor of 1.5 as compared to the traditional parallel sequential method. This block linear solver is, moreover, independent of discretization schemes and preconditioners used; thus further acceleration and higher accuracy can be expected when combined with other existing discretization schemes or preconditioners.
机译:众所周知,辐射传递方程(RTE)比其扩散近似(DA)提供更准确的层析成像结果。但是,基于RTE的断层重建代码由于计算成本高而在实践中受到限制。在本文中,我们提出了一种新的有效方法来一次性解决多个光源的RTE前向问题,而不是分别为每个光源解决。为此,我们在这里介绍一种新颖的线性求解器,称为块双共轭梯度稳定方法(块BiCGStab),该方法充分利用了不同右侧之间的共享信息来加速解收敛。提出了两种并行块BiCGStab方法,以在有限线程情况下实现额外的加速。我们通过数值模拟研究(包括对散射相位函数的Delta-Eddington逼近)评估该算法的性能。结果表明,与传统的顺序求解方法相比,本文提出的单线程块RTE求解器的计算时间减少了1.5〜3倍,并行块求解器的计算时间减少了1.5倍。此外,该块线性求解器独立于离散化方案和所使用的前置条件;因此,当与其他现有的离散化方案或预处理器结合使用时,可以期待进一步的加速和更高的精度。

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