SIRF: Synergistic Image Reconstruction Framework

Evgueni Ovtchinnikov, Richard Brown, Christoph Kolbitsch, Edoardo Pasca, Casper da Costa-Luis, Ashley G. Gillman, Benjamin A. Thomas, Nikos Efthimiou, Johannes Mayer, Palak Wadhwa, Matthias J. Ehrhardt, Sam Ellis, Jakob S. Jørgensen, Julian Matthews, Claudia Prieto, Andrew J. Reader, Charalampos Tsoumpas, Martin Turner, David Atkinson, Kris Thielemans

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Abstract

The combination of positron emission tomography (PET) with magnetic resonance (MR) imaging opens the way to more accurate diagnosis and improved patient management. At present, the data acquired by PET-MR scanners are essentially processed separately, but the opportunity to improve accuracy of the tomographic reconstruction via synergy of the two imaging techniques is an active area of research. In this paper, we present Release 2.1.0 of the CCP-PETMR Synergistic Image Reconstruction Framework (SIRF) software suite, providing an open-source software platform for efficient implementation and validation of novel reconstruction algorithms. SIRF provides user-friendly Python and MATLAB interfaces built on top of C++ libraries. SIRF uses advanced PET and MR reconstruction software packages and tools. Currently, for PET this is Software for Tomographic Image Reconstruction (STIR); for MR, Gadgetron and ISMRMRD; and for image registration tools, NiftyReg. The software aims to be capable of reconstructing images from acquired scanner data, whilst being simple enough to be used for educational purposes. The most recent version of the software can be downloaded from http://www.ccppetmr.ac.uk/downloads and https://github.com/CCPPETMR/. Program summary: Program Title: Synergistic Image Reconstruction Framework (SIRF) Program Files DOI: http://dx.doi.org/10.17632/s45f5jh55j.1 Licensing provisions: GPLv3 and Apache-2.0 Programming languages: C++, C, Python, MATLAB Nature of problem: In current practice, data acquired by PET-MR scanners are processed separately. Methods for improving the accuracy of the tomographic reconstruction using the synergy of the two imaging techniques are actively being investigated by the PET-MR research and development community, however, practical application is heavily reliant on software. Open-source software available to the PET-MR community – such as the PET package (STIR) (Thielemans et al., 2012) and the MR package Gadgetron (Hansen and Sørensen, 2013) – provide a basis for new synergistic PET-MR software. However, these two software packages are independent and have very different software architectures. They are mostly written in C++ but many researchers in the PET-MR community are more familiar with script-style languages, such as Python and MATLAB, which enable rapid prototyping of novel reconstruction algorithms. In the current situation it is difficult for researchers to exploit any synergy between PET and MR data. Furthermore, techniques from one field cannot easily be applied in the other. Solution method: In SIRF, the bulk of computation is performed by available advanced open-source reconstruction and registration software (currently STIR, Gadgetron and NiftyReg) that can use multithreading and GPUs. The SIRF C++ code provides a thin layer on top of these existing libraries. The SIRF layer has unified data-containers and access mechanisms. This C++ layer provides the basis for a simple and intuitive Python and MATLAB interface, enabling users to quickly develop and test their reconstruction algorithms using these scripting languages only. At the same time, advanced users proficient in C++ can directly utilise wider SIRF functionality via the SIRF C++ libraries that we provide.
Original languageEnglish
Article number107087
Number of pages1
JournalComputer Physics Communications
Volume249
Early online date5 Dec 2019
DOIs
Publication statusPublished - 30 Apr 2020

Cite this

Ovtchinnikov, E., Brown, R., Kolbitsch, C., Pasca, E., da Costa-Luis, C., Gillman, A. G., Thomas, B. A., Efthimiou, N., Mayer, J., Wadhwa, P., Ehrhardt, M. J., Ellis, S., Jørgensen, J. S., Matthews, J., Prieto, C., Reader, A. J., Tsoumpas, C., Turner, M., Atkinson, D., & Thielemans, K. (2020). SIRF: Synergistic Image Reconstruction Framework. Computer Physics Communications, 249, [107087]. https://doi.org/10.1016/j.cpc.2019.107087