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From radiobiology to radiotherapy: dose homogeneity in cells after alpha irradiation in measurements and Monte Carlo simulations

Filipek Mateusz, Pszczółkowska-Kępa Beata, Kociński Bartłomiej, Senkowska Marta, Szefliński Zygmunt, Brzozowska Beata

Polish Journal of Medical Physics and Engineering

2024

Vol. 30, Iss. 2

79--89

Introduction: Proton radiotherapy offers an advantage in sparing healthy tissue compared to photon therapy due to the specific interaction of protons with the patient’s body. In radiobiological experiments, alpha sources are commonly used instead of proton accelerators for convenience, but ensuring a uniform dose distribution is challenging. Properly designing the cell irradiation setup is crucial to reliably measure the average cellular response in such experiments. The objective of this research is to underscore the importance of dosimetric validation in radiobiological investigations. While Monte Carlo (MC) simulations offer valuable insights, their accuracy needs experimental confirmation. Once consistent results are obtained, the reliance on simulations becomes viable, as they are more efficient and less cumbersome compared to experimental procedures. Material and methods: The simulations are performed with three MC code-based tools: Geant4-DNA, GATE, and SRIM to model the alpha radiation source and calculate dose distributions for various cell irradiation scenarios. Dosimetric verification of the experimental setup containing a 241Am source is performed using radiochromic films. Additionally, a clonogenic cell survival assay is carried out for the DU145 cell line. Results: The study introduces a novel source simulation model derived from dosimetric measurements. The comparison between dosimetric results obtained with simulations and measured experimentally yields a gamma (3%/3mm) parameter value exceeding 99%. Furthermore, the LQ model parameters fitted to survival data of DU145 cells irradiated with particles emitted from 241Am source demonstrate consistency with previously published findings. Conclusions: Radiobiological experiments investigate cellular responses to various irradiation scenarios. Challenges arise with densely ionizing radiation used in clinical practice, particularly in ensuring uniform dose delivery for reliable experiments. MC codes aid in simulating dose distribution and designing irradiation systems for consistent cell treatment. However, experimental validation is essential before relying on simulation results. Once confirmed, these results offer a cost-effective and time-efficient approach to planning radiobiological experiments compared to traditional laboratory work.

Autoregressive model with double Pareto distributed noise

Woszczek Hubert Piotr, Wyłomańska Agnieszka

Mathematica Applicanda

2023

Vol. 51, no. 1

121--141

Time series models are a popular tool commonly used to describe time-varying phenomena. One of the most popular models is the Gaussian AR. However, when the data have outlier observations with "large" values, Gaussian models are not a good choice. We therefore abandon the assumption of normality of the data distribution and propose the AR model based on the double Pareto distribution. We introduce the estimators of the model's parameters, obtained by the maximum likelihood method. For this purpose, we use the Maclaurin series expansion and the Chebyshev polynomials expansion of the likelihood function. We compare the results with the Yule-Walker estimator in the finite variance case and with the modified Yule-Walker estimator in the infinite variance case. The accuracy of the results obtained was checked by Monte Carlo simulations.

Modele szeregów czasowych to popularne narzędzie powszechnie stosowane do modelowania zjawisk zmiennych w czasie. Najpopularniejszym modelem jest gaussowski model AR, który jest stacjonarny. Jednak gdy w danych występują obserwacje odstające o „dużych“ wartościach, modele gaussowskie nie są odpowiednim narzędziem do ich modelowania. Odchodzimy zatem od założenia o normalności rozkładu danych i proponujemy model AR oparty na podwójnym rozkładzie Pareto. Przedstawiamy estymatory parametrów modelu, uzyskane metodą największej wiarygodności. W tym celu wykorzystujemy rozwinięcie funkcji warogodności w szereg zmodyfikowanym estymatorem Yule-Walkera w przypadku nieskończonej wariancji. Poprwaność otrzymanych wyników została sprawdzona za pomocą symulacji Monte Carlo.