Arbitrarily large tomography with iterative algorithms on multiple GPUs using the TIGRE toolbox

Abstract

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.