Implementation and validation of time-of-flight PET image reconstruction module for listmode and sinogram projection data in the STIR library

Abstract

In this paper, we describe the implementation of support for time-of-flight (TOF) positron emission tomography (PET) for both listmode and sinogram data in the open source software for tomographic image reconstruction (STIR). We provide validation and performance characterization using simulated data from the open source GATE Monte Carlo toolbox, with TOF configurations spanning from 81.2 to 209.6 ps. The coincidence detector resolution was corrected for the timing resolution deterioration due to the contribution of the crystal length. Comparison between the reconstruction of listmode and sinogram data demonstrated good agreement in both TOF and non-TOF cases in terms of relative absolute error. To reduce the reconstruction time, we assessed the truncation of the TOF kernel along lines-of-response (LOR). Rejection of LOR elements beyond four times the TOF standard deviation provides significant acceleration of without compromising the image quality. Further narrowing of the kernel can provide extra time reduction but with the gradual introduction of error in the reconstructed images. As expected, TOF reconstruction performs better than non-TOF in terms of both contrast-recovery-coefficient (CRC) and signal-to-noise ratio (SNR). CRC achieves convergence faster with TOF, at lower noise levels. SNR with TOF was superior for early iterations, but with quick deterioration. Higher timing resolution further improved reconstruction performance, while TOF bin mashing was shown to have only a small impact on reconstructed images.

TOF-PET image reconstruction with multiple timing kernels applied on Cherenkov radiation in BGO

“…Software for Tomographic Image Reconstruction (STIR) (v.4.0) [45], [46] supports a wide range of reconstruction algorithms for the determination of the Maximum Likelihood Estimation (MLE). In this paper, we used TOF Listmode Maximum Likelihood-Expectation Maximisation (LM-MLEM) as it is the most robust option and is guaranteed to converge to a solution [47]- [49]. The validation of the TOF reconstruction with Gaussian [49]- [51] and non-Gaussian [52] kernels, has been presented in detail previously.…”

“…In this paper, we used TOF Listmode Maximum Likelihood-Expectation Maximisation (LM-MLEM) as it is the most robust option and is guaranteed to converge to a solution [47]- [49]. The validation of the TOF reconstruction with Gaussian [49]- [51] and non-Gaussian [52] kernels, has been presented in detail previously. In this study, the application of the TOF kernel is done in a similar manner to that of the simple Gaussian.…”

Processing of Compton events in the PETALO readout system

“…The reconstruction was performed using STIR [10] and the Python-STIR interface SIRF [11]. In particular, pre-release versions of STIR and SIRF were used which allowed for the inclusion of TOF in the reconstruction as implemented in [12]. Sinograms were produced by computing the intersection of each LOR with 64 φ-planes spanning axial angles from 0 to π.…”

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.…”