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 Jorgensen; Julian Matthews; Claudia Prieto; Andrew J. Reader; Charalampos Tsoumpas; Martin Turner; David  Atkinson; Kris  Thielemans

doi:10.1016/j.cpc.2019.107087

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 Jorgensen, 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 scannersare 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 opensource software platform for ecient implementation and validation of novel reconstruction algorithms. SIRF provides user-friendly Python and MATLAB binterfaces 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.

Original language	English
Journal	Computer Physics Communications
Early online date	5 Dec 2019
DOIs	https://doi.org/10.1016/j.cpc.2019.107087
Publication status	Published - 2020

Keywords

Image Reconstruction
PET-MR
Multi-modality
Medical Imaging
Open-Source Software

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.cpc.2019.107087

SIRF_CPC50_2020_submittedAccepted author manuscript, 1.74 MB

Tomographic Imaging CCPi
Withers, P. (PI), Lee, P. (CoI) & Lionheart, W. (CoI)
29/08/15 → 28/08/20
Project: Research

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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., Jorgensen, J., Matthews, J., Prieto, C., Reader, A. J., Tsoumpas, C., Turner, M., Atkinson, D., & Thielemans, K. (2020). SIRF: Synergistic Image Reconstruction Framework. Computer Physics Communications. https://doi.org/10.1016/j.cpc.2019.107087

@article{8fe1b7906e58482bb0806ce1704ded35,

title = "SIRF: Synergistic Image Reconstruction Framework",

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 scannersare 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 opensource software platform for ecient implementation and validation of novel reconstruction algorithms. SIRF provides user-friendly Python and MATLAB binterfaces 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.",

keywords = "Image Reconstruction, PET-MR, Multi-modality, Medical Imaging, Open-Source Software",

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

year = "2020",

doi = "10.1016/j.cpc.2019.107087",

language = "English",

journal = "Computer Physics Communications",

issn = "0010-4655",

publisher = "Elsevier BV",

}

Ovtchinnikov, E, Brown, R, Kolbitsch, C, Pasca, E, da Costa-Luis, C, Gillman, AG, Thomas, BA, Efthimiou, N, Mayer, J, Wadhwa, P, Ehrhardt, MJ, Ellis, S, Jorgensen, J, Matthews, J, Prieto, C, Reader, AJ, Tsoumpas, C, Turner, M, Atkinson, D & Thielemans, K 2020, 'SIRF: Synergistic Image Reconstruction Framework', Computer Physics Communications. https://doi.org/10.1016/j.cpc.2019.107087

TY - JOUR

T1 - SIRF: Synergistic Image Reconstruction Framework

AU - Ovtchinnikov, Evgueni

AU - Brown, Richard

AU - Kolbitsch, Christoph

AU - Pasca, Edoardo

AU - da Costa-Luis, Casper

AU - Gillman, Ashley G.

AU - Thomas, Benjamin A.

AU - Efthimiou, Nikos

AU - Mayer, Johannes

AU - Wadhwa, Palak

AU - Ehrhardt, Matthias J.

AU - Ellis, Sam

AU - Jorgensen, Jakob

AU - Matthews, Julian

AU - Prieto, Claudia

AU - Reader, Andrew J.

AU - Tsoumpas, Charalampos

AU - Turner, Martin

AU - Atkinson, David

AU - Thielemans, Kris

PY - 2020

Y1 - 2020

N2 - 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 scannersare 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 opensource software platform for ecient implementation and validation of novel reconstruction algorithms. SIRF provides user-friendly Python and MATLAB binterfaces 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.

AB - 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 scannersare 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 opensource software platform for ecient implementation and validation of novel reconstruction algorithms. SIRF provides user-friendly Python and MATLAB binterfaces 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.

KW - Image Reconstruction

KW - PET-MR

KW - Multi-modality

KW - Medical Imaging

KW - Open-Source Software

U2 - 10.1016/j.cpc.2019.107087

DO - 10.1016/j.cpc.2019.107087

M3 - Article

SN - 0010-4655

JO - Computer Physics Communications

JF - Computer Physics Communications

ER -

SIRF: Synergistic Image Reconstruction Framework

Abstract

Keywords

UN SDGs

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Projects

Tomographic Imaging CCPi

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