Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 1009.0832

Foundation of an analytical proton beamlet model for inclusion in a general proton dose calculation system

W. Ulmer·B. Schaffner
View original source ↗Read source document ↗Export metadata ↓

Abstract

We have developed a model for proton depth dose and lateral distributions based on Monte Carlo calculations (GEANT4) and an integration procedure of the Bethe-Bloch equation (BBE). The model accounts for the transport of primary and secondary protons, the creation of recoil protons and heavy recoil nuclei as well as lateral scattering of these contributions. The buildup, which is experimentally observed in higher energy depth dose curves, is modeled by inclusion of two different origins: 1. Secondary reaction protons with a contribution of ca. 65 % of the buildup (for monoenergetic protons). 2. Landau tails as well as Gaussian type of fluctuations for range straggling effects. All parameters of the model for initially monoenergetic proton beams have been obtained from Monte Carlo calculations or checked by them. Furthermore, there are a few parameters, which can be obtained by fitting the model to measured depth dose curves in order to describe individual characteristics of the beamline - the most important being the initial energy spread. We find that the free parameters of the depth dose model can be predicted for any intermediate energy from a couple of measured curves.

Explore related subjects

physics.med-ph

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

W. Ulmer, B. Schaffner. 2010-09-04. Foundation of an analytical proton beamlet model for inclusion in a general proton dose calculation system. https://doi.org/10.1016/j.radphyschem.2010.10.006

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Prediction of biological radiation effects based on ionization clusters (nanodosimetry)

This article reviews approaches that link the formation of ionization clusters in nanometric volumes to radiobiological effectiveness. The corresponding models were developed as the field of nanodosimetry developed. Some address early biological radiation effects, such as DNA damage, while most aim to predict cell survival or inactivation. The models also differ in the nanodosimetric quantities considered, with many based on the probability distribution of ionization cluster formation in a single target. Some models account for the synergistic effects of pairs of ionization clusters formed in different targets. Several models feature macroscopic aggregation frameworks based on particle fluence, which are proposed for use in radiotherapy treatment planning, particularly in ion-beam radiotherapy. The models are presented here using harmonized terminology and notation for nanodosimetric quantities. An extension of the conceptual framework of nanodosimetry is also discussed. This extension transitions from a target-centered description to a track-centered description. It also introduces nanodosimetry-based analogs of dosimetric concepts, such as dose and linear energy transfer. This paper traces and summarizes the historical development of nanodosimetry-based biological effect models and discusses conceptual aspects of the models to reveal their underlying assumptions and the extent to which they are mechanistic or merely elucidate correlations. Eventually, an attempt is made to identify the key open questions in this field that still need to be addressed.

physics.med-ph↗
arXiv2026

Contextual Cellular Growth (ConCeG) of neural cells for realistic grey matter tissue generation for diffusion MRI simulations

Accurate interpretation of diffusion magnetic resonance imaging (dMRI) signals in grey matter (GM) remains challenging due to the complex, heterogeneous, and densely packed cellular environment. Numerical phantoms provide a controlled framework for investigating the relationship between microstructure and diffusion signals, yet existing approaches often lack the morphological realism and multi-cellular organisation required to faithfully represent GM tissue. In this work, we introduce Contextual Cellular Growth (ConCeG), a generative framework for creating individual cells or constructing dense, three-dimensional, multi-cellular GM substrates informed by real neuronal and glial morphologies. The method combines topological neuron synthesis with a spatially constrained growth network, allowing for the controlled generation of heterogeneous cellular environments with realistic intra- and extracellular compartments. Synthetic cells are generated using morphological and topological characteristics derived from biological reconstructions. We validate the framework through comparisons of structural features with real cellular data, demonstrating strong agreement in branch order, length, angle, and tortuosity distributions. Power spectrum analysis further shows that both intracellular compartments reproduce the spatial correlations observed in biological tissue. Together, these results show ConCeG provides a biologically grounded framework for generating grey matter substrates suitable for large scale diffusion MRI simulation.

physics.med-ph↗
arXiv2026

Magnetic Field of Firing Neuron in Humans: Measurable by Quantum Sensing MRI?

Firing neurons generate action potentials that propagate along axons to transmit signals supporting cognitive functions. These electrical currents generate magnetic fields, yet direct detection of these neuronal magnetic fields by MRI remains elusive. This Mini Review investigates why this goal has been proven difficult to achieve and whether an emerging approach, quantum sensing MRI, can overcome the challenge.

physics.med-ph↗