Ovtchinnikov, E, Atkinson, D, Kolbitsch, C et al. (14 more authors) (2018) SIRF: Synergistic Image Reconstruction Framework. In: 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). NSS/MIC 2017: Nuclear Science Symposium and Medical Imaging Conference, 21-28 Oct 2017, Atlanta, GA, USA. IEEE ISBN 978-1-5386-2282-7
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 and MR scanners are essentially processed separately, and the search for ways to improve accuracy of the tomographic reconstruction via synergy of the two imaging techniques is an active area of research. The aim of the collaborative computational project on PET and MR (CCP-PETMR), supported by the UK engineering and physical sciences research council (EPSRC), is to accelerate research in synergistic PET-MR image reconstruction by providing an open access software platform for efficient implementation and validation of novel reconstruction algorithms. In this paper, we present the first release of the Synergistic Image Reconstruction Framework (SIRF) software suite from the CCP-PETMR. SIRF provides user-friendly Python and MATLAB interfaces to advanced PET and MR reconstruction packages written in C}++ textbf{{(currently this uses STIR, Software for Tomographic Image Reconstruction, for PET and Gadgetron for MR, but SIRF will be able to link to other reconstruction engines in the future as appropriate). The software is capable of reconstructing images from real scanner data. Both of the available integrated clinical PET-MR systems (Siemens and GE) are being targeted, and a suitable data format exchange is being negotiated with the manufacturers.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Keywords: | Positron Emission Tomography; Magnetic Resonance Imaging; Research Software Engineering; Scientific Programming |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Leeds Institute of Cardiovascular and Metabolic Medicine (LICAMM) > Biomedical Imaging Science Dept (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 08 Mar 2019 14:21 |
Last Modified: | 08 Mar 2019 14:21 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/NSSMIC.2017.8532815 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:143421 |