PARALLELPROJ -- An open-source framework for fast calculation of projections in tomography

TL;DR

This paper presents ParallelProj, an open-source framework that accelerates tomographic projections using CPUs and GPUs, enabling rapid PET image reconstruction with significant speedups over traditional CPU methods.

Contribution

The paper introduces ParallelProj, a novel open-source framework that efficiently performs parallel projections in tomography on CPUs and GPUs, including extensions for TOF PET projections.

Findings

GPU mode accelerates projections by 25-68 times compared to CPU mode.

PET reconstructions can be completed within seconds on a single consumer GPU.

ParallelProj integrates with STIR for improved performance.

Abstract

In this article, we introduce, a novel open-source framework designed for efficient parallel computation of projections in tomography leveraging either multiple CPU cores or GPUs. This framework efficiently implements forward and back projection functions for both sinogram and listmode data, utilizing Joseph's method, which is further extended to encompass time-of-flight (TOF) PET projections. Our evaluation involves a series of tests focusing on PET image reconstruction using data sourced from a state-of-the-art clinical PET/CT system. We thoroughly benchmark the performance of the projectors in non-TOF and TOF, sinogram, and listmode employing multi CPU-cores, hybrid CPU/GPU, and exclusive GPU mode. Moreover, we also investigate the timing of non-TOF sinogram projections calculated in STIR (Software for Tomographic Image Reconstruction) which recently integrated parallelproj as one of its projection backends. Our results indicate that the exclusive GPU mode provides acceleration factors between 25 and 68 relative to the multi-CPU-core mode. Furthermore, we demonstrate that OSEM listmode reconstruction of state-of-the-art real-world PET data sets is achievable within a few seconds using a single consumer GPU.