Monte Carlo evaluation of hypothetical long axial field-of-view PET scanner using GE discovery MI PET front-end architecture

Validation of a discovery MI 4-ring model according to the NEMA NU 2-2018 standards: from Monte Carlo simulations to clinical-like reconstructions

BACKGROUND

We propose a comprehensive evaluation of a Discovery MI 4-ring (DMI) model, using a Monte Carlo simulator (GATE) and a clinical reconstruction software package (PET toolbox). The following performance characteristics were compared with actual measurements according to NEMA NU 2-2018 guidelines: system sensitivity, count losses and scatter fraction (SF), coincidence time resolution (CTR), spatial resolution (SR), and image quality (IQ). For SR and IQ tests, reconstruction of time-of-flight (TOF) simulated data was performed using the manufacturer's reconstruction software.

RESULTS

Simulated prompt, random, true, scatter and noise equivalent count rates closely matched the experimental rates with maximum relative differences of 1.6%, 5.3%, 7.8%, 6.6%, and 16.5%, respectively, in a clinical range of less than 10 kBq/mL. A 3.6% maximum relative difference was found between experimental and simulated sensitivities. The simulated spatial resolution was better than the experimental one. Simulated image quality metrics were relatively close to the experimental results.

CONCLUSIONS

The current model is able to reproduce the behaviour of the DMI count rates in the clinical range and generate clinical-like images with a reasonable match in terms of contrast and noise.

Accurate modeling and performance evaluation of a total-body pet scanner using Monte Carlo simulations

BACKGROUND

The limited axial field-of-view (FOV) of conventional clinical positron emission tomography (PET) scanners (∼15 to 26 cm) allows detecting only 1% of all coincidence photons, hence limiting significantly their sensitivity. To overcome this limitation, the EXPLORER consortium developed the world's first total-body PET/CT scanner that significantly increased the sensitivity, thus enabling to decrease the scan duration or injected dose.

PURPOSE

The purpose of this study is to perform and validate Monte Carlo simulations of the uEXPLORER PET scanner, which can be used to devise novel conceptual designs and geometrical configurations through obtaining features that are difficult to obtain experimentally.

METHODS

The total-body uEXPLORER PET scanner was modeled using GATE Monte Carlo (MC) platform. The model was validated through comparison with experimental measurements of various performance parameters, including spatial resolution, sensitivity, count rate performance, and image quality, according to NEMA-NU2 2018 standards. Furthermore, the effects of the time coincidence window and maximum ring difference on the count rate and noise equivalent count rate (NECR) were evaluated.

RESULTS

Overall, the validation study showed that there was a good agreement between the simulation and experimental results. The differences between the simulated and experimental total sensitivity for the NEMA and extended phantoms at the center of the FOV were 2.3% and 0.0%, respectively. The difference in peak NECR was 9.9% for the NEMA phantom and 1.0% for the extended phantom. The average bias between the simulated and experimental results of the full-width-at-half maximum (FWHM) for six different positions and three directions was 0.12 mm. The simulations showed that using a variable coincidence time window based on the maximum ring difference can reduce the effect of random coincidences and improve the NECR compared to a constant time coincidence window. The NECR corresponding to 252-ring difference was 2.11 Mcps, which is larger than the NECR corresponding to 336-ring difference (2.04 Mcps).

CONCLUSION

The developed MC model of the uEXPLORER PET scanner was validated against experimental measurements and can be used for further assessment and design optimization of the scanner.

Autonomous digitizer calibration of a Monte Carlo detector model through evolutionary simulation

Simulating the response of a radiation detector is a modelling challenge due to the stochastic nature of radiation, often complex geometries, and multi-stage signal processing. While sophisticated tools for Monte Carlo simulation have been developed for radiation transport, emulating signal processing and data loss must be accomplished using a simplified model of the electronics called the digitizer. Due to a large number of free parameters, calibrating a digitizer quickly becomes an optimisation problem. To address this, we propose a novel technique by which evolutionary algorithms calibrate a digitizer autonomously. We demonstrate this by calibrating six free parameters in a digitizer model for the ADAC Forte. The accuracy of solutions is quantified via a cost function measuring the absolute percent difference between simulated and experimental coincidence count rates across a robust characterisation data set, including three detector configurations and a range of source activities. Ultimately, this calibration produces a count rate response with 5.8% mean difference to the experiment, improving from 18.3% difference when manually calibrated. Using evolutionary algorithms for model calibration is a notable advancement because this method is novel, autonomous, fault-tolerant, and achieved through a direct comparison of simulation to reality. The software used in this work has been made freely available through a GitHub repository.