Pontificia Universidad Católica de Chile Pontificia Universidad Católica de Chile
Ovtchinnikov E, Brown R, Kolbitsch C, Pasca E, Costa L, Thomas A, Efthimiou N, Mayer J, Wadhwa P, Ehrhardt J, Ellis S, Jorgense J, Matthews J, Prieto C, Reader AJ, Tsoumpas Ch, Turner M, Thielemans K. SIRF: Synergistic Image Reconstruction Framework. Computer Physics Communications 2020, 249, 107087, doi: 10.1016/j.cpc.2019.107087. (2020)

SIRF: Synergistic Image Reconstruction Framework.

Revista : Computer Physics Communications
Volumen : 249
Páginas : 107087
Tipo de publicación : Publicaciones WOS sin afiliación UC Ir a publicación


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/.