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 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.

(An overview of) Synergistic reconstruction for multimodality/multichannel imaging methods

“…A practical difficulty with synergistic methods, especially in multi-modality imaging, is the need for software that can handle large amounts of data, is capable to accurately compute the system models (4), and ideally allows easy experimentation with novel algorithms. It is therefore often necessary to combine several software packages, ideally via an overarching framework [132] or by writing interfaces to other packages such that they can be used in an optimisation library such as [133,134,135].…”

Impact of Time-of-Flight on Respiratory Motion Modelling using Non-Attenuation-Corrected PET

“…PET acquisitions were simulated using Software for Tomographic Image Reconstruction (STIR) [7], [8] through Synergistic Image Reconstruction Framework (SIRF) [9], [10] to forward project the input data to sinograms using the geometry of a GE Discovery 710 and, where relevant, a TOF resolution of 375ps similar to the GE Signa PET/MR (using TOF mashing to reduce computation time resulting in 13 TOF time bins of size 376.5ps). Attenuation was included in the simulation using the relevant mu-map generated by XCAT.…”

Versatile regularisation toolkit for iterative image reconstruction with proximal splitting algorithms

“…Our first application is to reconstruct Positron Emission Tomography (PET) data using the SPDHG 10 algorithm with the Kullback-Leibler (Poisson) data model and two different regularisers from CCPi-RGL: FGP-TV 8 and TGV. 12 We use STIR-SIRF 5,6 software to model realistic PET system blur and apply Poisson noise to the projection data generated from the thorax phantom (see Fig. 2).…”

“…3 In this paper, we introduce the CCPi-Regularisation Toolkit (CCPi-RGL) 4 for effective and efficient regularisation of iterative methods for application the reconstruction of large 3D tomographic datasets. We demonstrate how the CCPi-RGL toolkit can be used to reconstruct positron emission tomography (PET) data with the STIR-SIRF package 5,6 and also high-resolution synchrotron tomographic data using MPI-based Savu software. 7 2 CCPi-RGL software contents and methodology…”