8 papers · 1 filter
Active learning-based variance reduction for Monte Carlo simulations: A feasibility study for the nanodosimetry around a gold nanoparticle
Leo Thomas, Miriam Schwarze, Hans Rabus
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 opti…
Cluster Dose Prediction in Carbon Ion Therapy: Using Transfer Learning from a Pretrained Dose Prediction U-Net
Miriam Schwarze, Hui Khee Looe, Björn Poppe +2
The cluster dose concept offers an alternative to the radiobiological effectiveness (RBE)-based model for describing radiation-induced biological effects. This study examines the a…
Cross-Section-Based Scaling Method for Material-Specific Cluster Dose Calculations -- A Proof of Concept
Miriam Schwarze, Hui Khee Looe, Björn Poppe +2
Cross-section data unavailability for non-water materials in track structure simulation software necessitates nanodosimetric quantity transformation from water to other materials.…
Exploring Machine Learning Models for Physical Dose Calculation in Carbon Ion Therapy Using Heterogeneous Imaging Data -- A Proof of Concept Study
Miriam Schwarze, Hui Khee Looe, Björn Poppe +3
Background: Accurate and fast dose calculation is essential for optimizing carbon ion therapy. Existing machine learning (ML) models have been developed for other radiotherapy moda…
Radial dependence of ionization clustering around a gold nanoparticle
Leo Thomas, Miriam Schwarze, Hans Rabus
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 Car…
Nanodosimetric investigation of the track structure of therapeutic carbon ion radiation. Part 2: Detailed radiation transport and track structure simulation
Miriam Schwarze, Gerhard Hilgers, Hans Rabus
Previously reported nanodosimetric measurements of therapeutic-energy carbon ions penetrating simulated tissue have produced results that are incompatible with the predicted mean e…