Advertisement

EJNMMI Physics

, 2:A37 | Cite as

Ultra fast, accurate PET image reconstruction for the Siemens hybrid MR/BrainPET scanner using raw LOR data

  • Juergen Scheins
  • Christoph Lerche
  • Jon Shah
Open Access
Meeting abstract
  • 383 Downloads

Keywords

Graphic Processing Unit Data Interpolation Widespread Usage Reconstruction Software Computational Optimisation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Fast PET image reconstruction algorithms usually use a Line-of-Response (LOR) preprocessing step where the detected raw LOR data are interpolated either to evenly spaced sinogram projection bins or alternatively to a generic projection space as for example proposed by the PET Reconstruction Software Toolkit (PRESTO) [1]. In this way, speed-optimised, versatile geometrical projectors can be implemented for iterative image reconstruction independent of the underlying scanner geometry. However, all strategies of projection data interpolation unavoidably lead to a loss of original information and result in some degradation of image quality. Here, direct LOR reconstructions overcome this evident drawback at cost of a massively enhanced computational burden. Therefore, computational optimisation techniques are essential to make such demanding approaches attractive and economical for widespread usage in the clinical environment. In this paper, we demonstrate for the Siemens Hybrid MR/BrainPET with 240 million physical LORs that a very fast quantitative direct LOR reconstruction can be realized using a modified version of PRESTO. Now, PRESTO is also capable to directly use sets of symmetric physical LORs instead of interpolating LORs to a generic projection space. Exploiting basic scanner symmetries together with the technique of Single Instruction Multipe Data (SIMD) and Simultaneous Multi-Threading (SMT) results in an overall calculation time of 2-3 minutes per frame on a single multi-core machine, i.e. neither requiring a cluster of mutliple machines nor Graphics Processing Units (GPUs).

Copyright information

© Scheins et al; licensee Springer. 2015

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Authors and Affiliations

  • Juergen Scheins
    • 1
  • Christoph Lerche
    • 1
  • Jon Shah
    • 1
  1. 1.Forschungszentrum Jülich GmbHJülichGermany

Personalised recommendations