Publications by authors named "M Bues"

Article Synopsis
  • This study examines the timing of proton beam delivery in radiation therapy using a non-invasive method with a pixelated semiconductor detector.
  • The detector, AdvaPIX-Timepix3, recorded the arrival times and energies of secondary particles to analyze various time structures of the beam, achieving high precision in measurements.
  • Results showed that the time switch parameters were measured with less than 1.5% uncertainty, providing valuable insights into proton spill rates and pulse repetition times for different types of accelerators.
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. To enhance an in-house graphic-processing-unit accelerated virtual particle (VP)-based Monte Carlo (MC) proton dose engine (VPMC) to model aperture blocks in both dose calculation and optimization for pencil beam scanning proton therapy (PBSPT)-based stereotactic radiosurgery (SRS)..

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Background: Accurate and efficient dose calculation is essential for on-line adaptive planning in proton therapy. Deep learning (DL) has shown promising dose prediction results in photon therapy. However, there is a scarcity of DL-based dose prediction methods specifically designed for proton therapy.

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Background: Setup reproducibility of the tissue in the proton beam path is critical in maintaining the planned clinical target volume (CTV) dose coverage and sparing the organs at risk (OAR). In this study, we retrospectively evaluated radiation therapy dose reproducibility for proton pencil beam scanning (PBS) treatment of breast cancer patients with and without mask immobilization.

Methods: Ninety-four patients treated between January 2019 and September 2022 with at least one verification CT scan (V-CT) in treatment position were included for this study.

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Article Synopsis
  • The study aims to improve a GPU-accelerated Monte Carlo proton dose engine to effectively model aperture blocks in proton therapy.
  • A new module for simulating virtual particles through patient-specific aperture blocks was developed and validated against an established Monte Carlo code, achieving high accuracy in dose calculations.
  • The results showed a significant reduction in calculation time, allowing for efficient optimization of treatment plans while meeting all dose constraints for patients.
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