The general-purpose Geant4 Monte Carlo toolkit and its Geant4-DNA extension to investigate mechanisms underlying the FLASH effect in radiotherapy: Current status and challenges.

FLASH radiotherapy is a promising approach to cancer treatment that offers several advantages over conventional radiotherapy. With this novel technique, high doses of radiation are delivered in a short period of time, inducing the so-called FLASH effect - a phenomenon characterized by healthy tissue sparing without alteration of tumor control. The mechanisms behind the FLASH effect remain unknown. One way to approach this problem is to gain insight into the initial parameters that can distinguish FLASH from conventional irradiation by simulating particle transport in aqueous media using the general-purpose Geant4 Monte Carlo toolkit and its Geant4-DNA extension. This review article discusses the current status of Geant4 and Geant4-DNA simulations to investigate mechanisms underlying the FLASH effect, as well as the challenges faced in this research field. One of the primary challenges is to accurately simulate the experimental irradiation parameters. Another challenge is the temporal extension of the simulations. This review also focuses on two hypotheses to explain the FLASH effect - namely the oxygen depletion hypothesis and the inter-track interactions hypothesis - and discusses how the Geant4 toolkit can be used to investigate them. The aim of this review is to provide an overview of Geant4 and Geant4-DNA simulations for FLASH radiotherapy and to highlight the challenges that need to be overcome in order to better study the FLASH effect.

Modeling of scavenging systems in water radiolysis with Geant4-DNA.

PURPOSE: This paper presents the capabilities of the Geant4-DNA Monte Carlo toolkit to simulate water radiolysis with scavengers using the step-by-step (SBS) or the independent reaction times (IRT) methods. It features two examples of application areas: (1) computing the escape yield of H2O2 following a 60Co γ-irradiation and (2) computing the oxygen depletion in water irradiated with 1 MeV electrons. METHODS: To ease the implementation of the chemical stage in Geant4-DNA, we developed a user interface that helps define the chemical reactions and set the concentration of scavengers. The first application area example required two computational steps to perform water radiolysis using NO2- and NO3- as scavengers and a 60Co irradiation. The oxygen depletion computation technique for the second application area example consisted of simulating track segments of 1 MeV electrons and determining the radio-induced loss and gain of oxygen molecules. RESULTS: The production of H2O2 under variable scavenging levels is consistent with the literature; the mean relative difference between the SBS and IRT methods is 7.2 % ± 0.5 %. For the oxygen depletion 1 µs post-irradiation, the mean relative difference between both methods is equal to 9.8 % ± 0.3 %. The results in the microsecond scale depend on the initial partial pressure of oxygen in water. In addition, the computed oxygen depletions agree well with the literature. CONCLUSIONS: The Geant4-DNA toolkit makes it possible to simulate water radiolysis in the presence of scavengers. This feature offers perspectives in radiobiology, with the possibility of simulating cell-relevant scavenging mechanisms.

Population-based input function for TSPO quantification and kinetic modeling with [11C]-DPA-713.

INTRODUCTION: Quantitative positron emission tomography (PET) studies of neurodegenerative diseases typically require the measurement of arterial input functions (AIF), an invasive and risky procedure. This study aims to assess the reproducibility of [11C]DPA-713 PET kinetic analysis using population-based input function (PBIF). The final goal is to possibly eliminate the need for AIF. MATERIALS AND METHODS: Eighteen subjects including six healthy volunteers (HV) and twelve Parkinson disease (PD) subjects from two [11C]-DPA-713 PET studies were included. Each subject underwent 90 min of dynamic PET imaging. Five healthy volunteers underwent a test-retest scan within the same day to assess the repeatability of the kinetic parameters. Kinetic modeling was carried out using the Logan total volume of distribution (VT) model. For each data set, kinetic analysis was performed using a patient-specific AIF (PSAIF, ground-truth standard) and then repeated using the PBIF. PBIF was generated using the leave-one-out method for each subject from the remaining 17 subjects and after normalizing the PSAIFs by 3 techniques: (a) Weightsubject×DoseInjected, (b) area under AIF curve (AUC), and (c) Weightsubject×AUC. The variability in the VT measured with PSAIF, in the test-retest study, was determined for selected brain regions (white matter, cerebellum, thalamus, caudate, putamen, pallidum, brainstem, hippocampus, and amygdala) using the Bland-Altman analysis and for each of the 3 normalization techniques. Similarly, for all subjects, the variabilities due to the use of PBIF were assessed. RESULTS: Bland-Altman analysis showed systematic bias between test and retest studies. The corresponding mean bias and 95% limits of agreement (LOA) for the studied brain regions were 30% and ± 70%. Comparing PBIF- and PSAIF-based VT estimate for all subjects and all brain regions, a significant difference between the results generated by the three normalization techniques existed for all brain structures except for the brainstem (P-value = 0.095). The mean % difference and 95% LOA is -10% and ±45% for Weightsubject×DoseInjected; +8% and ±50% for AUC; and +2% and ± 38% for Weightsubject×AUC. In all cases, normalizing by Weightsubject×AUC yielded the smallest % bias and variability (% bias = ±2%; LOA = ±38% for all brain regions). Estimating the reproducibility of PBIF-kinetics to PSAIF based on disease groups (HV/PD) and genotype (MAB/HAB), the average VT values for all regions obtained from PBIF is insignificantly higher than PSAIF (%difference = 4.53%, P-value = 0.73 for HAB; and %difference = 0.73%, P-value = 0.96 for MAB). PBIF also tends to overestimate the difference between PD and HV for HAB (% difference = 32.33% versus 13.28%) and underestimate it in MAB (%difference = 6.84% versus 20.92%). CONCLUSIONS: PSAIF kinetic results are reproducible with PBIF, with variability in VT within that obtained for the test-retest studies. Therefore, VT assessed using PBIF-based kinetic modeling is clinically feasible and can be an alternative to PSAIF.

Monte Carlo Simulation of SARS-CoV-2 Radiation-Induced Inactivation for Vaccine Development.

Immunization with an inactivated virus is one of the strategies currently being tested towards developing a SARS-CoV-2 vaccine. One of the methods used to inactivate viruses is exposure to high doses of ionizing radiation to damage their nucleic acids. While gamma (γ) rays effectively induce lesions in the RNA, envelope proteins are also highly damaged in the process. This in turn may alter their antigenic properties, affecting their capacity to induce an adaptive immune response able to confer effective protection. Here, we modeled the effect of sparsely and densely ionizing radiation on SARS-CoV-2 using the Monte Carlo toolkit Geant4-DNA. With a realistic 3D target virus model, we calculated the expected number of lesions in the spike and membrane proteins, as well as in the viral RNA. Our findings showed that γ rays produced significant spike protein damage, but densely ionizing charged particles induced less membrane damage for the same level of RNA lesions, because a single ion traversal through the nuclear envelope was sufficient to inactivate the virus. We propose that accelerated charged particles produce inactivated viruses with little structural damage to envelope proteins, thereby representing a new and effective tool for developing vaccines against SARS-CoV-2 and other enveloped viruses.

Physical performance of a long axial field-of-view PET scanner prototype with sparse rings configuration: A Monte Carlo simulation study.

PURPOSE: There is a growing interest in extending the axial fields-of-view (AFOV) of PET scanners. One major limitation for the widespread clinical adoption of such systems is the multifold increase in the associated material costs. In this study, we propose a cost-effective solution to extend the PET AFOV using a sparse detector rings configuration. The corresponding physical performance was validated using Monte Carlo simulations. METHODS: Monte Carlo model of the Siemens BiographTM mCT PET/CT, with a 21.8 cm AFOV and a set of compact rings of LSO crystals was developed as a gold standard. The mCT configuration was then modified by interleaving the LSO crystals in the axial direction within each detector block with 4 mm physical gaps (equivalent to the LSO crystal axial dimension) thus extending the AFOV to 43.6 cm (Ex-mCT). The physical performances of the two MC models were assessed and then compared using NEMA NU 2-2007 standards. RESULTS: Ex-mCT showed <0.2 mm difference in transaxial spatial resolution, and, 0.8 mm and 0.3 mm deterioration in axial spatial resolution, compared to the mCT, at 1 and 10 cm off-center of the transaxial field-of-view respectively. The system sensitivities for the mCT and Ex-mCT models were 9.4 ± 0.2 and 10.75 ± 0.2 cps/kBq respectively. The higher sensitivity of Ex-mCT was due to four additional detector rings required to double the mCT AFOV. PET images of the NEMA Image Quality (IQ) phantom showed no artifacts due to detector rings sparsity, and all spheres were visible in both configurations. Ex-mCT achieved percent contrast recoveries within 5.6% of those of the mCT for all spheres and a maximum of 36% higher background variability at the center of the AFOV. The Ex-mCT, however, showed a more uniform noise distribution over an axial range of almost twice the length of the mCT AFOV. CONCLUSIONS: Using the proposed sparse detector-ring configuration, the AFOV of current generation PET systems can be doubled while maintaining the original number and volume of detector crystal elements, and without jeopardizing the system's overall physical performance. Despite an increase in the noise level, the Ex-mCT exhibited an improved noise uniformity.