Publications by authors named "Olaf Nackenhorst"

Article Synopsis
  • - The study focuses on using thermoluminescence detectors (TL-DOS) to gather detailed information about radiation exposure, not just the dosage, which helps enhance radiation safety measures.
  • - Researchers analyzed glow curves from these novel dosemeters using deep learning to accurately predict the date of a specific radiation exposure (10 mGy) over a 41-day monitoring period.
  • - The deep learning algorithm achieves prediction accuracy of 2-5 days, and the study also assesses the significance of various input features using Shapley values, enhancing the understanding of how the neural network makes predictions.
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Microbeam radiation therapy (MRT) utilizes coplanar synchrotron radiation beamlets and is a proposed treatment approach for several tumor diagnoses that currently have poor clinical treatment outcomes, such as gliosarcomas. Monte Carlo (MC) simulations are one of the most used methods at the Imaging and Medical Beamline, Australian Synchrotron to calculate the dose in MRT preclinical studies. The steep dose gradients associated with the 50μm-wide coplanar beamlets present a significant challenge for precise MC simulation of the dose deposition of an MRT irradiation treatment field in a short time frame.

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Purpose: Novel radiotherapy techniques like synchrotron X-ray microbeam radiation therapy (MRT) require fast dose distribution predictions that are accurate at the sub-mm level, especially close to tissue/bone/air interfaces. Monte Carlo (MC) physics simulations are recognized to be one of the most accurate tools to predict the dose delivered in a target tissue but can be very time consuming and therefore prohibitive for treatment planning. Faster dose prediction algorithms are usually developed for clinically deployed treatments only.

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