Monte Carlo model validation of a detector system used for Positron Emission Particle Tracking

Herald, Matthew T; Wheldon, Tzany Kokalova; Windows-Yule, Kit

doi:10.1016/j.nima.2021.165073

Cited by 18 publications

(25 citation statements)

References 29 publications

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“…Additionally, the two detector heads can be moved between 250 mm and 800 mm of separation which can accommodate a variety of experiments 18,19 . A full description of this model and its original calibration can be found in Herald, et al (2021) 8 . The Forte and the GATE model of the Forte are shown in Figure 1.…”

Section: Gate Modelmentioning

confidence: 99%

“…Several detectors have been modelled using GATE and validated against experimental measurements such as the Phillips/ADAC Forte, Siemens Inveon, and Phillips Vereos Positron Emission Tomography (PET) scanners [8][9][10] . For PET systems, performance characterisation experiments are described by the National Electronics Manufacturers Associated (NEMA) which test the spatial resolution, sensitivity, and count-rate response 11 .…”

Section: Introductionmentioning

confidence: 99%

“…In this work, we propose a calibration procedure which uses an evolutionary algorithm to modify the free parameters of the digitizer in order produce a model which reproduces the coincidence count rate responses measured in characterisation experiments. We demonstrate this approach by calibrating the GATE digitizer model of the Phillips/ADAC Forte, a dual-headed positron camera operated in coincidence mode 8 . The Forte and its digital-twin GATE model are shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

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Autonomous Digitizer Calibration of a Monte Carlo Detector Model through Evolutionary Simulation

Herald

Nicușan

Wheldon

et al. 2022

Preprint

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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 using evolutionary algorithms to perform digitizer calibrations. We demonstrate this by calibrating a digitizer for the Phillips/ACAC Forte, which contains six free parameters. 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 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.

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Section: Gate Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Autonomous Digitizer Calibration of a Monte Carlo Detector Model through Evolutionary Simulation

Herald

Nicușan

Wheldon

et al. 2022

Preprint

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“…The University of Birmingham 15 . The Monte Carlo model was written using the GATE software package 17 , used to replicate the characteristics of positron emission and scanners through the use of validated physics models.…”

Section: A Simulated Gate Datamentioning

confidence: 99%

An expectation-maximization algorithm for positron emission particle tracking

Manger,

Renaud,

Vanneste

2021

Preprint

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Positron Emission Particle Tracking (PEPT) is an imaging method that tracks individual radioactive particles. PEPT relies on the detection of back-to-back photon pairs emitted by positron annihilation. It requires an algorithm to locate the radioactive particles based on the set of lines defined by successive photon-pair detections.We propose and test a new algorithm for this task.The algorithm relies on the maximization of a likelihood arising from a simple Gaussian-mixture model defined in the space of lines. The model includes a component that accounts for spurious lines caused by scattering and random coincidence, and treats the relative activity of particles as well as their positions as parameters to be inferred. Values of these parameters that approximately maximize the likelihood are computed by application of an expectation-maximization algorithm. A generalization of the model that includes the particle velocities and accelerations as additional parameters takes advantage of the information contained in the exact timing of positron annihilations to reconstruct pieces of trajectories rather than fixed positions, with clear benefits.We test the algorithm on both simulated and experimental data. The results show the algorithm to be highly effective for the simultaneous tracking of many particles (up to 80 in one test). It provides estimates of particle positions that are easily mapped to entire trajectories and handles a variable number of particles in the field of view. The ability to track a large number of particles robustly offers the possibility of a dramatic expansion of the scope of PEPT

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“…Many steps in the digitizer model correspond directly to measurable properties of the detector, such as the energy resolution, dead-time, or time resolution 10 . Values for these properties are often provided by the manufacturer and this can serve as a reliable starting point, but manual tuning is still needed to match the simulated and experimental response of the detector due to variation between each detector 8 . Conversely, with a model-fitting approach, manual tuning can be avoided, but other challenges arise.…”

Section: Introductionmentioning

confidence: 99%

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

Herald

Nicușan

Wheldon

et al. 2022

Sci Rep

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Monte Carlo model validation of a detector system used for Positron Emission Particle Tracking

Cited by 18 publications

References 29 publications

Autonomous Digitizer Calibration of a Monte Carlo Detector Model through Evolutionary Simulation

Autonomous Digitizer Calibration of a Monte Carlo Detector Model through Evolutionary Simulation

An expectation-maximization algorithm for positron emission particle tracking

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

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