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Fast iterative image reconstruction methods for fully 3D multispectral bioluminescence tomography

机译:全3D多光谱生物发光层析成像的快速迭代图像重建方法

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摘要

We investigate fast iterative image reconstruction methods for fully 3D multispectral bioluminescence tomography for applications in small animal imaging. Our forward model uses a diffusion approximation for optically inhomogeneous tissue, which we solve using a finite element method (FEM). We examine two approaches to incorporating the forward model into the solution of the inverse problem. In a conventional direct calculation approach one computes the full forward model by repeated solution of the FEM problem, once for each potential source location. We describe an alternative on-the-fly approach where one does not explicitly solve for the full forward model. Instead, the solution to the forward problem is included implicitly in the formulation of the inverse problem, and the FEM problem is solved at each iteration for the current image estimate. We evaluate the convergence speeds of several representative iterative algorithms. We compare the computation cost of those two approaches, concluding that the on-the-fly approach can lead to substantial reductions in total cost when combined with a rapidly converging iterative algorithm.
机译:我们研究了用于小动物成像的全3D多光谱生物发光层析成像的快速迭代图像重建方法。我们的正向模型对光学非均质组织使用扩散近似,我们使用有限元方法(FEM)进行求解。我们研究了将正向模型纳入反问题解决方案的两种方法。在常规的直接计算方法中,对于每个潜在的源位置,通过重复求解FEM问题来计算完整的正向模型。我们描述了一种动态的替代方法,其中没有明确解决完整的正向模型。取而代之的是,前向问题的解决方案隐含在反问题的制定中,并且在当前图像估计的每次迭代中都解决了FEM问题。我们评估了几种代表性迭代算法的收敛速度。我们比较了这两种方法的计算成本,得出结论,与快速收敛的迭代算法结合使用时,动态方法可以导致总成本的大幅降低。

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