arXiv subjects

Leo Thomas

Publications and source records attributed to Leo Thomas.

8 recordsLinked to original sources

arXiv2025

Active learning-based variance reduction for Monte Carlo simulations: A feasibility study for the nanodosimetry around a gold nanoparticle

Objective: This work presents a data-driven importance sampling-based variance reduction (VR) scheme inspired by active learning. The method is applied to the estimation of an optimal impact-parameter distribution in the calculation of ionization clusters around a gold nanoparticle (NP). Here, such an optimal importance distribution can not be inferred from principle. Approach: An iterative optimization procedure is set up that uses a Gaussian Process Sampler to propose optimal sampling distributions based on a loss function. The loss is constructed based on appropriate heuristics. The optimization code obtains estimates of the number of ionization clusters in shells around the NP by interfacing with a Geant4 simulation via a dedicated Transmission Control Protocol (TCP) interface. Main results: It is shown that the so-derived impact-parameter distribution easily outperforms the actual, uniform irradiation case. The results resemble those obtained with other VR schemes but do still slightly overestimate background contributions. Significance: While the method presented is a proof-of-principle, it provides a novel method of estimating importance distributions in ill-posed scenarios. The presented TCP interface described here is a simple and efficient method to expose compiled Geant4 code to other scripts, written for example, in Python.

physics.med-ph↗

arXiv2025

Cluster Dose Prediction in Carbon Ion Therapy: Using Transfer Learning from a Pretrained Dose Prediction U-Net

The cluster dose concept offers an alternative to the radiobiological effectiveness (RBE)-based model for describing radiation-induced biological effects. This study examines the application of a neural network to predict cluster dose distributions, with the goal of replacing the computationally intensive simulations currently required. Cluster dose distributions are predicted using a U-Net that was initially pretrained on conventional dose distributions. Using transfer learning techniques, the decoder path is adapted for cluster dose estimation. Both the training and pretraining datasets include head and neck regions from multiple patients and carbon ion beams of varying energies and positions. Monte Carlo (MC) simulations were used to generate the ground truth cluster dose distributions. The U-Net enables cluster dose estimation for a single pencil beam within milliseconds using a graphics processing unit (GPU). The predicted cluster dose distributions deviate from the ground truth by less than 0.35%. This proof-of-principle study demonstrates the feasibility of accurately estimating cluster doses within clinically acceptable computation times using machine learning (ML). By leveraging a pretrained neural network and applying transfer learning techniques, the approach significantly reduces the need for large-scale, computationally expensive training data.

physics.med-ph↗

arXiv2025

Cross-Section-Based Scaling Method for Material-Specific Cluster Dose Calculations -- A Proof of Concept

Cross-section data unavailability for non-water materials in track structure simulation software necessitates nanodosimetric quantity transformation from water to other materials. Cluster dose calculation transformation initially employed mass-density-based scaling - an approach resulting in a physically unrealistic material-independence of the cluster dose equation. This study introduces an alternative scaling method based on material-specific ionization cross-sections. The mean free path ratio of the materials for both the primary particles of the track structure simulation and for the secondary electrons served as the scaling factor. The approach was demonstrated through a cluster dose calculation for a carbon ion beam in a realistic head geometry and compared to the previous scaling method. The proposed cross-section-based scaling method resulted in a physically expected increase in cluster dose values for denser materials, which was not visible in the original scaling approach. The introduced scaling approach can be used to determine cluster dose distributions in heterogeneous geometries, a fundamental requirement for its integration into radiotherapy treatment planning frameworks.

physics.med-ph↗

arXiv2025

Exploring Machine Learning Models for Physical Dose Calculation in Carbon Ion Therapy Using Heterogeneous Imaging Data -- A Proof of Concept Study

Background: Accurate and fast dose calculation is essential for optimizing carbon ion therapy. Existing machine learning (ML) models have been developed for other radiotherapy modalities. They use patient data with uniform CT imaging properties. Purpose: This study investigates the application of several ML models for physical dose calculation in carbon ion therapy and compares their ability to generalize to CT data with varying resolutions. Among the models examined is a Diffusion Model, which is tested for the first time for the calculation of physical dose distributions. Methods: A dataset was generated using publicly available CT images of the head and neck region. Monoenergetic carbon ion beams were simulated at various initial energies using Geant4 simulation software. A U-Net architecture was developed for dose prediction based on distributions of material density in patients and of absorbed dose in water. It was trained as a Generative Adversarial Network (GAN) generator, a Diffusion Model noise estimator, and as a standalone network. Their performances were compared with two models from literature. Results: All models produced dose distributions deviating by less than 2% from that obtained by a full Monte Carlo simulation, even for a patient not seen during training. Dose calculation time on a GPU was in the range of 3 ms to 15 s. The resource-efficient U-Net appears to perform comparably to the more computationally intensive GAN and Diffusion Model. Conclusion: This study demonstrates that ML models can effectively balance accuracy and speed for physical dose calculation in carbon ion therapy. Using the computationally efficient U-Net can help conserve resources. The generalizability of the models to different CT image resolutions enables the use for different patients without extensive retraining.

physics.med-ph↗

arXiv2025

Impact of metal nanoparticles on cell survival predicted by the local effect model for cells in suspension and tissue. Part 1: Theoretical framework

This work investigates the change in cell survival predicted by the local effect model (LEM) for an irradiated cell containing metal nanoparticles (MNPs) depending on the distribution of neighboring cells and the uptake of MNPs into the cells. In this first part of the paper, the theoretical framework is described, which is based on analytical weighting functions for the energy deposition around a single metal nanoparticle and radially symmetric distributions of MNPs. The weighting functions allow calculation of the radial profile of the absorbed dose in the cell nucleus as well as the mean dose and the mean square of the dose in the nucleus. The latter two quantities determine cell survival according to the LEM. The weighting functions are applied to isolated cells in a localized MNP distribution, cells in solution, and densely packed cells in tissue. It is shown that only for the idealistic case of complete uptake of MNPs it is sufficient to consider an isolated cell, as this otherwise leads to a significant underestimation in more realistic situations. In the case of cells in tissue, the MNP concentration within the range of secondary particles around the cell must be taken into account. Different packing densities of the cells may lead to values differing by up to 30% for the mean dose in the cell nucleus, depending on the conceived scenario for the uptake of MNPs. The weighting function offers a versatile method for assessing cell survival under irradiation in the presence of MNPs by the LEM, which is more general than previously reported approaches.

physics.med-ph↗

arXiv2024

On a revised concept of an event that allows linking nanodosimetry and microdosimetry in nanometric sites with macroscopic dosimetry

This work reviews the concepts of an event used in micro- and nanodosimetry and analyzes how single event distributions could theoretically be derived from probability distributions related to interactions of the primary particle which produce secondary electrons. It is shown that the corresponding mathematical expressions of conditional ionization cluster size distributions are alike those for the single event frequency distribution of energy imparted, particularly when all tracks are considered which intersect the volume in which interactions of the primary particle can result in energy deposits in the site. Track structure simulations of proton with energies between 1 MeV and 100 MeV are used to study how the occurrence of events depends on site size, beam radius, and proton energy. The range of impact parameters of particle tracks that contribute to energy imparted in a site appears not to depend on whether any energy deposits or only energy deposits by ionizations are considered. Since there is no longer a one-to-one correspondence between tracks passing a site and the occurrence of an event, it is proposed to use the fluence for which on average one event occurs as a substitute for single events. For protons, the product of this fluence and the site cross section or the average number of tracks necessary for an event shows an interesting dependence on site size and particle energy with asymptotic values close to unity for large sites and proton energies below 10 MeV. For a proton energy of 1 MeV, a minimum of the number of tracks is observed for sites between 5 nm and 10 nm diameter. The relative differences between the numbers of track per event on average obtained with different options of Geant4-DNA are in the order of 10 % and illustrate the need for further investigations into cross-section datasets and their uncertainties.

physics.med-ph↗

arXiv2024

Radial dependence of ionization clustering around a gold nanoparticle

This work explores the enhancement of ionization clusters around a gold nanoparticle (NP), indicative of the induction of DNA lesions, a potential trigger for cell-death. Monte Carlo track structure simulations were performed to determine (a) the fluence of incident photons and electrons in water around a gold NP under charged particle equilibrium conditions and (b) the density of ionization clusters produced on average as well as conditional on the occurrence of at least one interaction in the nanoparticle using Associated Volume Clustering. Absorbed dose was determined for comparison with a recent benchmark intercomparison. Reported quantities are normalized to primary fluence, allowing to establish a connection to macroscopic dosimetric quantities. The modification of the electron fluence spectrum by the gold NP is minor and mainly occurs at low energies. The net fluence of electrons emitted from the NP is dominated by electrons resulting from photon interactions. Smaller NPs cause noticeable peaks in the conditional frequency of clusters at distances around 50 nm to 100 nm from the NP surface. The number of clusters per energy imparted is increased at distances of up to 150 nm, and accordingly the enhancement in clustering notably surpasses that of dose enhancement. This work highlights the necessity of nanodosimetric analysis and suggests increased ionization clustering near the nanoparticles due to the emission of low energy Auger electrons. Whereas the electron component of the radiation field plays an important role in determining the background contribution to ionization clustering and energy imparted, the dosimetric effects of nanoparticles are governed by the interplay of secondary electron production by photon interaction (including low energy Auger electrons) and their ability to leave the nanoparticle.

physics.med-ph↗

arXiv2023

Article Commentary on "Microdosimetric and radiobiological effects of gold nanoparticles at therapeutic radiation energies" [T.M. Gray et al., IJRB 2023, 99(2), 308-317]

In the recently published article by T.M. Gray et al. "Microdosimetric and radiobiological effects of gold nanoparticles at therapeutic radiation energies" (IJRB 2023, 99(2), 308-317) results of Monte Carlo simulations and radiobiological assays on the dosimetric effects of gold nanoparticles were presented. This commentary points out that the results of the two parts of the study are in contradiction and that the predicted magnitude of dose enhancement and its dependence on the shape of the nanoparticle appear implausible. Possible reasons for these observations are discussed.

physics.med-ph↗