Processing optimization with parallel computing for the J-PET tomography scanner

TL;DR

This paper discusses the application of parallel computing techniques to optimize data processing and reconstruction in the J-PET scanner, a novel TOF-PET detector based on polymer scintillators, to handle its high data demands efficiently.

Contribution

It introduces the use of parallel computing methods specifically tailored for the data processing challenges of the J-PET detector.

Findings

Parallel computing significantly speeds up data processing.

Optimized algorithms improve reconstruction efficiency.

Enhanced data handling for triggerless acquisition mode.

Abstract

The Jagiellonian-PET (J-PET) collaboration is developing a prototype TOF-PET detector based on long polymer scintillators. This novel approach exploits the excellent time properties of the plastic scintillators, which permit very precise time measurements. The very fast, FPGA-based front-end electronics and the data acquisition system, as well as, low- and high-level reconstruction algorithms were specially developed to be used with the J-PET scanner. The TOF-PET data processing and reconstruction are time and resource demanding operations, especially in case of a large acceptance detector, which works in triggerless data acquisition mode. In this article, we discuss the parallel computing methods applied to optimize the data processing for the J-PET detector. We begin with general concepts of parallel computing and then we discuss several applications of those techniques in the J-PET data processing.