AI-Based Dose Predictions in Radiotherapy
Explore the traceability of AI-based dose predictions in radiotherapy for improved treatment planning and personalized care. Learn about the objectives, methodologies, and potential impact of machine learning in dosimetry and radiation protection. Join the discussion on developing guidelines for testing the performance of AI-based dose predictions and enabling multi-target treatment planning. This informative content is provided by Physikalisch-Technische Bundesanstalt and Hans Rabus.
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Presentation Transcript
Traceability for AI-based dose predictions in radiotherapy & al. Hans Rabus
Machine Learning for dosimetry Radiation protection: online dosimetry Nuclear medicine: Activity from images & dose evaluation Radiation Therapy: accurate and fast , of course Denoising undersampled MC simulations Learning transformation of distributions (e.g., CT+beam dose) GAN-based approaches Goal: Quasi-MC quality of TPs and real time dose adaption 06.11.2024 TC-IR Brainstorming Meeting 2
Traceability of AI-based dose predictions Objectives Develop a framework for assessing the uncertainty involved in AI-based dose predictions Analyze the state of the art and develop guidelines for harmonized testing of the performance of such dose predictions Assess the potential of AI-based approaches for multi-target treatment planning (e.g. biology based) Enable fully personalized radiotherapy 06.11.2024 TC-IR Brainstorming Meeting 3
About Physikalisch-Technische Bundesanstalt Braunschweig and Berlin Bundesallee 100 38116 Braunschweig Hans Rabus Telefon:0531 592-7054 E-Mail: hans.rabus@ptb.de www.ptb.de Status: 10/2024
