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55th SSRMP Annual Meeting

27–28 Oct 2022
Kultur- und Kongresszentrum Thun
Europe/Zurich timezone

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Session

Poster

27 Oct 2022, 11:35
Lachensaal & Foyer (Kultur- und Kongresszentrum Thun)

Lachensaal & Foyer

Kultur- und Kongresszentrum Thun

Seestrasse 68 CH-3604 Thun

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  1. 38. Evaluating a deep learning based 3D dose prediction model for quality assurance of organ at risk contours.
    Mr Amith Kamath (ARTORG Center for Biomedical Research, University of Bern, Bern, Switzerland)
    27/10/2022, 11:35
    Radiation Therapy
    Poster

    Purpose: One of the recent methods to improve the efficiency, consistency and overall plan quality in radiotherapy is to use dose prediction. Based on prior experience of similar cases, deep neural networks can predict an achievable dose for a specific case. Instead of planning purposes, we are exploring the possibilities of using dose prediction in the QA of automatic segmentation. For...

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  2. 42. Industrial Exhibition
    27/10/2022, 11:35
    Miscellaneous
  3. 3. Pilot studies using an ESHO quality assurance phantom for phased-array deep hyperthermia devices
    Mr Dietmar Marder (Kantonsspital Aarau, RadioOnkologieZentrum KSA-KSB)
    27/10/2022, 11:45
    Miscellaneous
    Poster

    Introduction
    Phantoms for quality assurance (QA) of deep hyperthermia therapy (DHT) devices are not commercially available. In this work, we suggest an easy to build phantom to perform commissioning and regular QA on the performance of DHT phased-array devices.
    Methods
    The phantom container consists of a 64cm long polyurethane tube with an external diameter of 25cm and a wall thickness of...

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  4. 12. Study of the influence of optimisation parameters on the Pareto front for prostate cancer
    Diana Wüthrich (Institute of Radiation Physics, Lausanne University Hospital, Lausanne, Switzerland)
    27/10/2022, 11:55
    Radiation Therapy
    Poster

    Purpose: To study the influence of objective functions and weights, as well as mean doses to secondary organs at risk (OAR) on Pareto front robustness.

    Methods: We wrote and validated a python script that controls RayStation (RaySearch) treatment planning system (TPS) and calculates Pareto fronts. Then, we randomly chose thirty-one prostate cancer patients treated at our clinic and...

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