3D tomographic imaging requires the computation of solutions to very large inverse problems. In many applications, iterative algorithms provide superior results, however, memory limits in available computing hardware restrict the size of problems that can be solved. For this reason, iterative methods are not normally used to reconstruct typical data sets acquired with lab based CT systems. We thus use state of the art techniques such as dual buffering to develop an efficient strategy to compute the required operations for iterative reconstruction. This allows the iterative reconstruction of volumetric images of arbitrary size using any number of GPUs, each with arbitrarily small memory. Strategies for both the forward and backprojection operators are presented, along with two regularization approaches that are easily generalized to other projection types or regularizers. The proposed improvement also accelerates reconstruction of smaller images on single or multiple GPU systems, providing faster code for time-critical applications. The resulting algorithm has been added to the TIGRE toolbox, a repository for iterative reconstruction algorithms for general CT, but this memory-saving and problem-splitting strategy can be easily adapted for use with other GPU-based tomographic reconstruction code.

3D断层成像需要计算非常大的反问题的解。在许多应用中，迭代算法可提供出色的结果，但是，可用计算硬件中的内存限制限制了可以解决的问题的大小。因此，通常不使用迭代方法来重建通过基于实验室的CT系统获取的典型数据集。因此，我们使用最先进的技术（例如双重缓冲）来开发一种有效的策略，以计算迭代重建所需的运算。这允许使用任意数量的GPU（每个都具有任意小的内存）来迭代重建任意大小的体积图像。提出了前向和后向投影算子的策略，以及两种可以很容易地推广到其他投影类型或调节器的调节方法。提出的改进还加速了在单个或多个GPU系统上较小图像的重建，从而为时间紧迫的应用程序提供了更快的代码。生成的算法已添加到TIGRE工具箱中，该库是用于常规CT的迭代重建算法的存储库，但是这种节省内存和解决问题的策略可以轻松地与其他基于GPU的层析重建代码一起使用。