Evaluation of quantitative, efficient image reconstruction for VersaPET, a compact PET system

Abstract

Purpose: Previously we developed a high-resolution positron emission tomography (PET) system-VersaPET-characterized by a block geometry with relatively large axial and transaxial interblock gaps and a compact geometry susceptible to parallax blurring effects. In this work, we report the qualitative and quantitative evaluation of a graphic processing unit (GPU)-accelerated maximum-likelihood by expectation-maximization (MLEM) image reconstruction framework for VersaPET which features accurate system geometry and projection space point-spread-function (PSF) modeling. Methods: We combined the ray-tracing module from software for tomographic image reconstruction (STIR), an open-source PET image reconstruction package, with VersaPET's exact block geometry for the geometric system matrix. Point-spread-function modeling of crystal penetration and scattering was achieved by a custom Monte-Carlo simulation for projection space blurring in all dimensions. We also parallelized the reconstruction in GPU taking advantage of the system's symmetry for PSF computation. To investigate the effects of PSF width, we generated and studied multiple kernels between one that reflects the true LYSO density in the MC simulation and another that reflects geometry only (no PSF). GATE simulations of hot and cold-sphere phantoms with spheres of different sizes, real microDerenzo phantom, and human blood vessel data were used to characterize the quantitative and qualitative performances of the reconstruction. Results: Reconstruction with an accurate system geometry effectively improved image quality compared to STIR (version 3.0) which assumes an idealized system geometry. Reconstructions of GATEsimulated hot-sphere phantom data showed that all PSF kernels achieved superior performance in contrast recovery and bias reduction compared to using no PSF, but may introduce edge artifact and lumped background noise pattern depending on the width of PSF kernels. Cold-sphere phantom simulation results also indicated improvement in contrast recovery and quantification with PSF modeling (compared to no PSF) for 5 and 10 mm cold spheres. Real microDerenzo phantom images with the PSF kernel that reflects the true LYSO density showed degraded resolving power of small sectors that could be resolved more clearly by underestimated PSF kernels, which is consistent with recent literature despite differences in scanner geometries and in approaches to system model estimation. The human vessel results resemble those of the hot-sphere phantom simulation with the PSF kernel that reflects the true LYSO density achieving the highest peak in the time activity curve (TAC) and similar lumped noise pattern. Conclusions: We fully evaluated a practical MLEM reconstruction framework that we developed for VersaPET in terms of qualitative and quantitative performance. Different PSF kernels may be adopted for improving the results of specific imaging tasks but the underlying reasons for the variation in optimal kernel for the real and simulation studies requires further study.

Recent advances in combined Positron Emission Tomography and Magnetic Resonance Imaging

“…Since these matrices require high loads of memory storage, symmetries and SRM sparsity have been exploited to reduce the SRM size. Some authors applied PSF descriptions [185,[204][205][206][207], or wide tubes-of-response (TORs) [208][209][210][211] instead of simple projection models. Other approaches include separate positron range correction, which is included in the reconstruction as an image blurring kernel [212,213].…”

UMC-PET: a fast and flexible Monte Carlo PET simulator

“…They have also been used to include realistic physical models in the reconstruction process to improve image quality and help reducing artifacts. For example, the system response matrix (SRM) has been approximated with different approaches using MC simulations (Herraiz et al 2006, Gillam and Rafecas 2016, Wei and Vaska 2020, and image corrections such as scatter inside the patient body (Castiglioni et al 1999, Ma et al 2020, scatter inside detectors (Lee et al 2018, Peng et al 2018, or positron range modeling (Kraus et al 2012, Cal-González et al 2015, Cal-Gonzalez et al 2018 have been addressed with MC methods. Particle therapy also benefits from MC simulations during PET (and other imaging techniques, such as Prompt Gamma detection) for non-invasive dose monitoring.…”