arXiv subjects

Lennart Volz

Publications and source records attributed to Lennart Volz.

7 recordsLinked to original sources

arXiv2026

DoseRAD2026 Challenge dataset: AI accelerated photon and proton dose calculation for radiotherapy

Purpose: Accurate dose calculation is essential in radiotherapy for precise tumor irradiation while sparing healthy tissue. With the growing adoption of MRI-guided and real-time adaptive radiotherapy, fast and accurate dose calculation on CT and MRI is increasingly needed. The DoseRAD2026 dataset and challenge provide a public benchmark of paired CT and MRI data with beam-level photon and proton Monte Carlo dose distributions for developing and evaluating advanced dose calculation methods. Acquisition and validation methods: The dataset comprises paired CT and MRI from 115 patients (75 training, 40 testing) treated on an MRI-linac for thoracic or abdominal lesions, derived from the SynthRAD2025 dataset. Pre-processing included deformable image registration, air-cavity correction, and resampling. Ground-truth photon (6 MV) and proton dose distributions were computed using open-source Monte Carlo algorithms, yielding 40,500 photon beams and 81,000 proton beamlets. Data format and usage notes: Data are organized into photon and proton subsets with paired CT-MRI images, beam-level dose distributions, and JSON beam configuration files. Files are provided in compressed MetaImage (.mha) format. The dataset is released under CC BY-NC 4.0, with training data available from April 2026 and the test set withheld until March 2030. Potential applications: The dataset supports benchmarking of fast dose calculation methods, including beam-level dose estimation for photon and proton therapy, MRI-based dose calculation in MRI-guided workflows, and real-time adaptive radiotherapy.

physics.med-ph↗

arXiv2026

Mixed Ion Beams Enable Simultaneous Treatment and Real-Time Imaging in Carbon Ion Therapy

Carbon ion therapy is one of the most advanced forms of radiotherapy, promising improved efficacy against resistant cancers. However, the high precision offered by the carbon ion Bragg peak requires precise knowledge of the beam range inside the patient. We report the first experimental realization of range monitoring and portal imaging with a mixed ion beam, where carbon ions are treating the tumor while helium ions simultaneously accelerated to the same velocity fully traverse the patient and provide treatment feedback. Using the GSI synchrotron, a beam of 12C3+ and 4He1+ ions is accelerated, exploiting their nearly identical charge-to-mass ratios. Stable extraction with controlled helium fractions down to 7% is demonstrated. Beam characterization reveals that the helium ion Bragg peak can be cleanly separated from the carbon ion fragment background which enables accurate detection of sub-millimeter Bragg peak displacements. Mixed-beam radiographs of a lung-cancer-like phantom offer target position detection to better than 0.5 mm accuracy. This establishes mixed beams as a powerful modality for real-time image guidance in carbon ion therapy, uniquely providing simultaneous treatment delivery, range probing, and portal imaging. By overcoming range uncertainty inside the patient, mixed beams will enable to fully exploit the precision of carbon ion therapy.

physics.med-ph↗

arXiv2026

Dosimetric Study of Lung Modulation and Motion Effects in Carbon ion Therapy for Lung Cancer

Carbon-ion radiotherapy provides high dose conformity for lung cancer, but its benefit is limited by two sources of uncertainties: interplay between scanned beam delivery and tumor motion, and dose modulation from heterogeneous lung tissue. This study quantifies the separate and combined dosimetric impact of these effects using the GSI TRiP4D treatment planning system. Eighteen lung cancer 4DCT datasets from TCIA were analyzed. A modulation power ($P_{\mathrm{mod}}$) was assigned to lung voxels. Three values were sampled from a Gaussian distribution ($200μ\mathrm{m} \pm 67μ\mathrm{m}$), and an extreme value of $750μ\mathrm{m}$ was tested. Interplay doses were computed by combining scanned-beam delivery with patient-specific respiratory motion. Four scenarios were studied: static, static with modulation, interplay, and interplay with modulation. Metrics included $D95\%$, $V95\%$, homogeneity index (HI), lung $V16\mathrm{Gy}$, and heart $V20\mathrm{Gy}$. Interplay reduced target coverage by $5.2 \pm 1.5$ pp ($D95\%$), $12.1 \pm 5.9$ pp ($V95\%$), and $8.3 \pm 2.4$ pp (HI). Extreme $P_{\mathrm{mod}}$ alone caused small degradations. When combined with interplay, it partially compensated the loss. This effect decreased with 4D optimization. Fractionation mitigated interplay, leaving lung modulation as the main residual effect.

physics.med-ph↗

arXiv2026

An AI dose engine for fast carbon ion treatment planning

Monte Carlo (MC) simulations provide gold-standard accuracy for carbon ion therapy dose calculations but are computationally intensive. Analytical pencil beam algorithms offer speed but reduced accuracy in heterogeneous tissues. We developed the first AI-based dose engine capable of predicting absorbed dose, the alpha and beta parameters for relative biological effectiveness (RBE)- weighted optimisation in carbon ion therapy, delivering MC-level accuracy with drastically reduced computation time. We extended the transformer-based DoTA model to predict absorbed dose (C-DoTA-d), alpha (C-DoTA-alpha), and beta (C-DoTA-beta), introducing a cross-attention mechanism for alpha and beta to combine dose and energy inputs. The training dataset consisted of ~70,000 pencil beams from 187 head-and-neck patients, with ground-truth values obtained using the GPU-accelerated MC toolkit FRED. Performance was evaluated on an independent test set using gamma pass rate (1%/1 mm), depth-dose, and isodose contour Dice coefficients. MC dropout-based uncertainty analysis was performed. Median gamma pass rates exceeded 98% for all predictions (99.76% for dose, 99.14% for alpha, and 98.74% for beta), with minima above 85% in the most heterogeneous anatomies. The Dice coefficient was 0.95 for 1% isodose contours, with slightly reduced agreement in high-gradient regions. Compared to MC FRED, inference was over 400x faster (0.032 s vs. 14 s per pencil beam) while maintaining accuracy. Uncertainty analysis showed high stability, with mean standard deviations below 0.5% for all models. C-DoTA achieves MC-quality predictions of absorbed dose and RBE model parameters in ~30 milliseconds per beam. Its speed and accuracy support online adaptive planning, paving the way for more effective carbon ion therapy workflows. Future work will expand to additional anatomical sites, beam geometries, and clinical beamlines.

physics.med-ph↗

arXiv2023

Exploration of Differentiability in a Proton Computed Tomography Simulation Framework

Objective. Algorithmic differentiation (AD) can be a useful technique to numerically optimize design and algorithmic parameters by, and quantify uncertainties in, computer simulations. However, the effectiveness of AD depends on how "well-linearizable" the software is. In this study, we assess how promising derivative information of a typical proton computed tomography (pCT) scan computer simulation is for the aforementioned applications. Approach. This study is mainly based on numerical experiments, in which we repeatedly evaluate three representative computational steps with perturbed input values. We support our observations with a review of the algorithmic steps and arithmetic operations performed by the software, using debugging techniques. Main results. The model-based iterative reconstruction (MBIR) subprocedure (at the end of the software pipeline) and the Monte Carlo (MC) simulation (at the beginning) were piecewise differentiable. Jumps in the MBIR function arose from the discrete computation of the set of voxels intersected by a proton path. Jumps in the MC function likely arose from changes in the control flow that affect the amount of consumed random numbers. The tracking algorithm solves an inherently non-differentiable problem. Significance. The MC and MBIR codes are ready for the integration of AD, and further research on surrogate models for the tracking subprocedure is necessary.

physics.med-ph↗

arXiv2019

Optimal particle type and projection number in multi-modality relative stopping power acquisition

Proton radiography combined with X-ray computed tomography (CT) has been proposed to obtain a patient-specific calibration curve and reduce range uncertainties in cancer treatment with charged particles. The main aim of this study was to identify the optimal charged particle for the generation of the radiographies and to determine the optimal number of projections necessary to obtain minimal range errors. Three charged particles were considered to generate the radiographies: proton (p-rad), helium ions (α-rad) and carbon ions (c-rad). The problem formulation was viewed as a least square optimization, argmin_x (||Ax -b||_2^2 ), where the projection matrix A represents the cumulative path crossed by each particle in each material, b is the charged particle list-mode radiography expressed as the crossed water equivalent thickness (WET) and x is the relative stopping power (RSP) map to be optimized. The charged particle radiographies were simulated using the Geant4 Monte Carlo (MC) toolkit and an anthropomorphic adult head phantom. A was determined by calculating the particles trajectory through the X-ray CT employing a convex hull detection combined with a cubic spline path. For all particle types, the impact of using multiple projections was assessed (from 1 to 6 projections). In this study, tissue segmentation methods were also investigated in terms of achievable accuracy. The best results (mean RSP error below 0.7% and range errors below 1 mm) were obtained for a-rad when 3 projections were used. The dose delivered to the phantom by a single α-rad was 8 \muGy, lower than the dose of X-ray radiography.

physics.med-ph↗

arXiv2016

A theoretical framework to predict the most likely ion path in particle imaging

In this work, a generic rigorous Bayesian formalism is introduced to predict the most likely path of any ion crossing a medium between two detection points. The path is predicted based on a combination of the particle scattering in the material and measurements of its initial and final position, direction and energy. The path estimate's precision is compared to the Monte Carlo simulated path. Every ion from hydrogen to carbon is simulated in two scenarios to estimate the accuracy achievable: one where the range is fixed and one where the initial velocity is fixed. In the scenario where the range is kept constant, the maximal root-mean-square error between the estimated path and the Monte Carlo path drops significantly between the proton path estimate (0.50 mm) and the helium path estimate (0.18 mm), but less so up to the carbon path estimate (0.09 mm). In the scenario where the initial velocity is kept constant, helium have systematically the minimal root-mean-square error throughout the path. As a result, helium is found to be the optimal particle for ion imaging.

physics.med-ph↗