Software for Tomographic Image Reconstruction (STIR: http://stir.sf.net) is an open source C++ library available for reconstruction of emission tomography data. This work aims at the incorporation of the GE SIGNA PET/MR scanner in STIR and enables PET image reconstruction with data corrections. The data extracted from the scanner after an acquisition includes a list of raw data files (emission, normalisation, geometric and well counter calibration (wcc) factors), magnetic resonance attenuation correction (MRAC) images and the scanner-based reconstructions. The listmode (LM) file stores a list of ’prompt’ events and the singles per crystal per second. MRAC images from the scanner are used for attenuation correction. The modifications to STIR that allow accurate histogramming of this LM data in the same sinogram organisation as the scanner are also described. This allows reconstruction of acquisition data with all data corrections using STIR, and independent of any software supplied by the manufacturer. The implementations were validated by comparing the histogrammed data, data corrections and final reconstruction using the ordered subset expectation maximisation (OSEM) algorithm with the equivalents from the GE-toolbox, supplied by the manufacturer for the scanner. There is no difference in the histogrammed counts whereas an overall relative difference of 6.7 × 10−8% and from 0.01% to 0.86% is seen in the normalisation and randoms correction sinograms respectively. The STIR reconstructed images have similar resolution and quantification but have some residual differences due to wcc factors, decay and deadtime corrections, as well as the offset between PET and MR gantries that will be addressed in future work. This work will enable the use of all current and future STIR algorithms, including penalized image reconstruction, motion correction and direct parametric image estimation, on data from GE SIGNA PET/MR scanners.
[1]
Alexander M. Grant,et al.
NEMA NU 2-2012 performance studies for the SiPM-based ToF-PET component of the GE SIGNA PET/MR system.
,
2016,
Medical physics.
[2]
A Geissbuhler,et al.
A normalization technique for 3D PET data.
,
1991,
Physics in medicine and biology.
[3]
Kris Thielemans,et al.
Implementation and validation of time-of-flight PET image reconstruction module for listmode and sinogram projection data in the STIR library
,
2019,
Physics in medicine and biology.
[4]
C. Tsoumpas,et al.
STIR: software for tomographic image reconstruction release 2
,
2012,
2006 IEEE Nuclear Science Symposium Conference Record.
[5]
E. Hoffman,et al.
3-D phantom to simulate cerebral blood flow and metabolic images for PET
,
1990
.
[6]
Frederic H Fahey,et al.
Data acquisition in PET imaging.
,
2002,
Journal of nuclear medicine technology.
[7]
C. Stearns,et al.
Random coincidence estimation from single event rates on the Discovery ST PET/CT scanner
,
2003,
2003 IEEE Nuclear Science Symposium. Conference Record (IEEE Cat. No.03CH37515).
[8]
Thomas Beyer,et al.
Quality control for quantitative multicenter whole-body PET/MR studies: A NEMA image quality phantom study with three current PET/MR systems.
,
2015,
Medical physics.