FPGA Acceleration of Ray-Based Iterative Algorithm for 3D Low-Dose CT Reconstruction

In this work, we aim to accelerate the iterative reconstruction (IR) algorithm used for 3D low dose computer tomography (LDCT) reconstruction to reduce the long execution time from an order of several hours on CPU to a few minutes. IR algorithms such as Mumford-Shah (MS) regularization can be used to get high-quality images even though the signal-noise ratio (SNR) of low dose projection data is low. However, IR is a computation and memory-intensive application and the long execution time precludes its clinical application. We adopt the ray-based parallel algorithm and designed a customized processing engine with multiple parallel processing elements (PEs) on field-programmable gate array (FPGA) to improve the computation efficiency. To reduce resource utilization, we proposed a best-first search algorithm combined with pruning to find the optimal bit width for fixed-point reconstruction. Besides, an offline memory optimization framework based on a greedy based clustering algorithm is proposed to reduce external memory bandwidth requirement and balance the workload of parallel PEs. Experiments on a 3D Shepp-Logan phantom show 2.81X and 1.91X speedup over the state of art single GPU and FPGA implementation.