Systematic Parallelization of Medical Image Reconstruction for Graphics Hardware

TLDR: This work uses High-Level Petri Nets (HLPN) to intuitively describe the parallel implementations for distributed- memory machines and identifies parallel functions that can be implemented efficiently on the GPU.

Abstract: Modern Graphics Processing Units (GPUs) consist of several SIMD-processors and thus provide a high degree of parallelism at low cost. We introduce a new approach to systematically develop parallel image reconstruction algorithms for GPUs from their parallel equivalents for distributed-memory machines. We use High-Level Petri Nets (HLPN) to intuitively describe the parallel implementations for distributed- memory machines. By denoting the functions of the HLPN with memory requirements and information about data distribution, we are able to identify parallel functions that can be implemented efficiently on the GPU. For an important iterative medical image reconstruction algorithm --the list-mode OSEM algorithm--we demonstrate the limitations of its distributed-memory implementation and show how our HLPN-based approach leads to a fast implementation on GPUs, reusable across different medical imaging devices.