Compton-based prompt gamma (PG) imaging has been proposed for in vivo range verification in proton therapy. However, several factors degrade the image quality of PG images, some of which are due to inherent properties of a Compton camera such as spatial resolution and energy resolution. Moreover, Compton-based PG imaging has a spatially variant resolution loss. In this study, we investigate the performance of the list-mode ordered subset expectation maximization algorithm with a shift-variant point spread function (LM-OSEM-SV-PSF) model. We also evaluate how well the PG images reconstructed using an SV-PSF model reproduce the distal falloff of the proton beam. The SV-PSF parameters were estimated from simulation data of point sources at various positions. Simulated PGs were produced in a water phantom irradiated with a proton beam. Compared to the LM-OSEM algorithm, the LM-OSEM-SV-PSF algorithm improved the quality of the reconstructed PG images and the estimation of PG falloff positions. In addition, the 4.44 and 5.25 MeV PG emissions can be accurately reconstructed using the LM-OSEM-SV-PSF algorithm. However, for the 2.31 and 6.13 MeV PG emissions, the LM-OSEM-SV-PSF reconstruction provides limited improvement. We also found that the LM-OSEM algorithm followed by a shift-variant Richardson-Lucy deconvolution could reconstruct images with quality visually similar to the LM-OSEM-SV-PSF-reconstructed images, while requiring shorter computation time.
This study demonstrates the feasibility of using various CC designs and event selection methods to improve the imaging of PG rays. In our designed two-stage CCs, the thin LYSO-based absorber can provide better predictions of the distal falloff positions than the thick one. Compared to three-stage CCs, two-stage CCs are less biased and provide more accurate range verification.
Non-pure positron emitters, with their long half-lives, allow for the tracing of slow biochemical processes which cannot be adequately examined by the commonly used short-lived positron emitters. Most of these isotopes emit high-energy cascade gamma rays in addition to positron decay that can be detected and create a triple coincidence with annihilation photons. Triple coincidence is discarded in most scanners, however, the majority of the triple coincidence contains true photon pairs that can be recovered. In this study, we propose a strategy for recovering triple coincidence events to raise the sensitivity of PET imaging for non-pure positron emitters. To identify the true line of response (LOR) from a triple coincidence, a framework utilizing geometrical, energy and temporal information is proposed. The geometrical criterion is based on the assumption that the LOR with the largest radial offset among the three sub pairs of triple coincidences is least likely to be a true LOR. Then, a confidence time window is used to test the valid LOR among those within triple coincidence. Finally, a likelihood ratio discriminant rule based on the energy probability density distribution of cascade and annihilation gammas is established to identify the true LOR. An Inveon preclinical PET scanner was modeled with GATE (GEANT4 application for tomographic emission) Monte Carlo software. We evaluated the performance of the proposed method in terms of identification fraction, noise equivalent count rates (NECR), and image quality on various phantoms. With the inclusion of triple coincidence events using the proposed method, the NECR was found to increase from 11% to 26% and 19% to 29% for I-124 and Br-76, respectively, when 7.4-185 MBq of activity was used. Compared to the reconstructed images using double coincidence, this technique increased the SNR by 5.1-7.3% for I-124 and 9.3-10.3% for Br-76 within the activity range of 9.25-74 MBq, without compromising the spatial resolution or contrast. We conclude that the proposed method can improve the counting statistics of PET imaging for non-pure positron emitters and is ready to be implemented on current PET systems.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.