Search arXivSearch

arXiv · 1211.3648

Microdosimetry of radiation field from therapeutic C-12 beam in water: a study with Geant4 toolkit

Abstract

We model the responses of Tissue-Equivalent Proportional Counters (TEPC) to radiation fields of therapeutic C-12 beams in a water phantom and to quasi-monoenergetic neutrons in a PMMA phantom. Simulations are performed with the Monte Carlo model for Heavy Ion Therapy (MCHIT) based on the Geant4 toolkit. The shapes of the calculated lineal energy spectra agree well with measurements in both cases. The influence of fragmentation reactions on the TEPC response to a narrow pencil-like beam with its width smaller than the TEPC diameter is investigated by Monte Carlo modeling. It is found that total lineal energy spectra are not very sensitive to the choice of the nuclear fragmentation model used. The calculated frequency-mean lineal energy y_f differs from the data on the axis of a therapeutic beam by less than 10% and by 10-20% at other TEPC positions. The validation of MCHIT with neutron beams gives us confidence in estimating the contributions to lineal energy spectra due to secondary neutrons produced in water by C-12 nuclei. As found, the neutron contribution at 10 cm distance from the beam axis amounts to ~ 50% close the entrance to the phantom and decreases to ~ 25% at the depth of the Bragg peak and beyond it. The presented results can help in evaluating biological out-of-field doses in carbon-ion therapy.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Lucas Burigo, Igor Pshenichnov, Igor Mishustin, Marcus Bleicher. 2013-03-11. Microdosimetry of radiation field from therapeutic C-12 beam in water: a study with Geant4 toolkit. https://doi.org/10.1016/j.nimb.2013.05.021

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

KEEP EXPLORING

Related papers

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

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

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