Framework for synergistic reconstruction of biomedical image data


Evgueni Ovtchinnikov1, Richard Brown2, Edoardo Pasca1, Casper da Costa-Luis2, Christoph Kollbitsch3, David Atkinson4, Johannes Meyer3, Kris Thielemans5

Estimated cost



Stable release v2.2 (GPL v3)

CCP SyneRBI is a collaborative computational project in synergistic reconstruction for biomedical imaging.

Its main software package SIRF is an open-source framework for synergistic image reconstruction. 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.

SIRF provides an open-source software platform for efficient implementation and validation of novel reconstruction algorithms. It features a user-friendly Python and MATLAB interfaces built on top of C++ libraries.

SIRF wraps advanced PET and MR reconstruction software packages and tools. Currently, for PET this is Software for Tomographic Image Reconstruction (STIR) and Gadgetron and ISMRMRD for MR. Image registration tools are currently provided by NiftyReg. Other modalities including SPECT are planned for the near future. The framework is tightly coupled to an optimization library (Core Imaging Library, CIL) providing a large variety of optimization methods.

The software aims to be capable of reconstructing images from acquired scanner data, whilst being simple enough to be used for educational purposes.



1Scientific Computing Department, Rutherford-Appleton Laboratory, UK Research and Innovation, Harwell Campus, UK

2 School Biomedical Engineering and Imaging Sciences, King’s College London, London, UK

3Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany

4Centre for Medical Imaging, University College London, London, UK

5 Institute of Nuclear Medicine, University College London, London, UK