arXiv subjects

Hermann Fuchs

Publications and source records attributed to Hermann Fuchs.

6 recordsLinked to original sources

arXiv2026

PyDoseRT Proton: A GPU Pencil-Beam Engine with a Convolutional Residual-Correction Network for Fast Proton Dose Calculation

Architecture category. Hybrid method: a physics-based analytical pencil-beam (PB) dose engine followed by a 3-D convolutional residual-correction network (RepVGG-U-Net). We addressed the DoseRAD2026 proton dose-prediction task with PyDoseRT Proton, a GPU-accelerated engine implemented in PyTorch and augmented by a learned residual toward Monte Carlo (MC) accuracy. A double-Gaussian PB kernel was calibrated to GATE/Geant4 integrated depth doses in water in two stages: a classical per-energy curve fit, then a gradient-based fit of the full 3-D dose through the PyTorch physics engine as it retains a differentiable execution path for gradient-based optimization of dose-dependent objectives. The engine computes each beamlet on a beam's-eye-view (BEV) lattice with variance-preserving Gaussian splitting, an analytic nuclear halo, and a Fermi-Eyges heterogeneity term, then rotates the result into the patient frame. Additionally, a compact residual U-Net predicts an additive correction in BEV space. It is conditioned on voxelwise material-label embeddings, a discrete energy embedding and spot size. The same model was used for all anatomical sites (thoracic and abdominal). It was trained with a patient-space L1 objective emphasizing the scored high-dose region and multi-scale BEV deep supervision. The submitted CT configuration obtained preliminary-test beamlet MAE 0.0066, image-z IDD distance 0.0025, plan MAE 0.0049, 98.30\% gamma pass rate (1\%/1 mm), and DVH error 0.460.

physics.med-ph↗

arXiv2026

PyDoseRT Photon: Physics-Guided Pencil-Beam Dose Calculation with Neural Priors and Residual Correction for CT and MRI

We present a hybrid, physics-based analytical pencil-beam (PB) dose engine, augmented by two small frozen neural physics priors, followed by a 3-D convolutional residual-correction network (U-Net). We address the DoseRAD2026 (https://doserad2026.grand-challenge.org/) photon dose-prediction task with PyDoseRT Photon, a GPU PB engine implemented in PyTorch and corrected toward Monte Carlo (MC) accuracy in three learned stages of decreasing physical specificity. The engine reproduces the challenge's head-less MC source exactly where it can and models the patient with a beam-quality-indexed pencil kernel evaluated at the field's fluence-weighted radiological depth, and TERMA source scaling at the interaction site. Two tiny neural priors are trained through the frozen engine and then frozen themselves: a 39k-parameter 2-D fluence correction and a 48-parameter lateral heterogeneity correction mixing mass-conserving Gaussian redistribution operators. A compact 3-D U-Net (1.36M parameters) with a sequential refinement branch then predicts, per control point (CP) in the beam's-eye-view (BEV) frame, a bounded multiplicative gain and additive residual from seven channels. All learned stages are zero-initialized, so training starts from the analytical solution. For MRI, an nnU-Net regression model synthesizes a CT that enters the identical pipeline, with consecutively, the same trained corrector as the CT track. Design choices were driven by the challenge ranking, in which runtime carries double weight. The submitted method evaluated on our local CT and MR validation dataset achieved CP MAE 0.0086 and 0.0098, IDD distance 0.0011 and 0.0013, plan MAE 0.0024 and 0.0047, gamma pass rate (1%/1mm) 99.12% and 96.95%, with runtimes of 46s and 49s, respectively.

physics.med-ph↗

arXiv2026

Noninvasive ion fraction quantification of dual-species beams in synchrotrons

The ion composition of dual-species beams in synchrotrons is typically inferred from invasive measurements performed after beam extraction. This paper introduces a complementary noninvasive method to determine the ion composition of such beams directly inside the synchrotron. The approach is applicable to low- and medium-energy synchrotrons and to small relative mass-to-charge ratio offsets, typically at the 1e-4 level. The method exploits dispersive orbit offsets between the two species and corresponding frequency corrections applied by the synchrotron RF radial regulation loop. This capability is of particular interest for ongoing research on online monitoring in carbon ion beam therapy using mixed 4He2+ and 12C6+ beams, which feature a relative mass-to-charge ratio offset of 0.065%. The proposed method is analytically derived and tested with particle tracking simulations using Xsuite. Its applicability under realistic experimental conditions is demonstrated at the MedAustron facility using mixed 4He2+ and 12C6+ beams. The results show good agreement with independent post-extraction measurements.

physics.acc-ph↗

arXiv2025

GATE 10 Monte Carlo particle transport simulation -- Part II: architecture and innovations

Over the past years, we have developed GATE version 10, a major re-implementation of the long-standing Geant4-based Monte Carlo application for particle and radiation transport simulation in medical physics. This release introduces many new features and significant improvements, most notably a Python-based user interface replacing the legacy static input files. The new functionality of GATE version 10 is described in the part 1 companion paper. The development brought significant challenges. In this paper, we present the solutions that we have developed to overcome these challenges. In particular, we present a modular design that robustly manages the core components of a simulation: particle sources, geometry, physics processes, and data acquisition. The architecture consists of parts written in C++ and Python, which needed to be coupled. We explain how this framework allows for the precise, time-aware generation of primary particles, a critical requirement for accurately modeling positron emission tomography (PET), radionuclide therapies, and prompt-gamma timing systems. We present how GATE 10 handles complex Geant4 physics settings while exposing a simple interface to the user. Furthermore, we describe the technical solutions that facilitate the seamless integration of advanced physics models and variance reduction techniques. The architecture supports sophisticated scoring of physical quantities (such as Linear Energy Transfer and Relative Biological Effectiveness) and is designed for multithreaded execution. The new user interface allows researchers to script complex simulation workflows and directly couple external tools, such as artificial intelligence models for source generation or detector response. By detailing these architectural innovations, we demonstrate how GATE 10 provides a more powerful and flexible tool for research and innovation in medical physics.

physics.med-ph↗

arXiv2025

GATE 10 Monte Carlo particle transport simulation -- Part I: development and new features

We present GATE version 10, a major evolution of the open-source Monte Carlo simulation application for medical physics, built on Geant4. This release marks a transformative evolution, featuring a modern Python-based user interface, enhanced multithreading and multiprocessing capabilities, the ability to be embedded as a library within other software, and a streamlined framework for collaborative development. In this Part 1 paper, we outline GATE's position among other Monte Carlo codes, the core principles driving this evolution, and the robust development cycle employed. We also detail the new features and improvements. Part 2 will detail the architectural innovations and technical challenges. By combining an open, collaborative framework with cutting-edge features, such a Monte Carlo platform supports a wide range of academic and industrial research, solidifying its role as a critical tool for innovation in medical physics.

physics.med-ph↗

arXiv2025

A double multi-turn injection scheme for generating mixed helium and carbon ion beams at medical synchrotron facilities

The low relative charge-to-mass ratio offset of 0.065% between fully ionized helium-4 and carbon-12 ions enables simultaneous acceleration in hadron therapy synchrotrons. At the same energy per mass, helium ions exhibit a stopping range approximately three times greater than carbon ions. They can therefore be exploited for online range verification downstream of the patient during carbon ion beam irradiation. One possibility for creating this mixed beam is accelerating the two ion species sequentially through the LINAC and subsequently "mixing" them at injection energy in the synchrotron with a double multi-turn injection scheme. This work reports the first successful generation, acceleration, and extraction of a mixed helium and carbon ion beam using this double multi-turn injection scheme, which was achieved at the MedAustron therapy accelerator in Austria. A description of the double multi-turn injection scheme, particle tracking simulations, and details on the implementation at the MedAustron accelerator facility are presented and discussed. Finally, measurements of the mixed beam at delivery in the irradiation room using a radiochromic film and a low-gain avalanche diode (LGAD) detector are presented.

physics.acc-ph↗