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07:31
Characterization of Recombination Effects in a Liquid Ionization Chamber Used for the Dosimetry of a Radiosurgical Accelerator
Published on: May 9, 2014
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A novel convolution-based approach to address ionization chamber volume averaging effect in model-based treatment
Brendan Barraclough1, Jonathan G Li, Sharon Lebron
1Department of Radiation Oncology, College of Medicine, University of Florida, Gainesville, FL 32611, USA. J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USA.
Physics in Medicine and Biology
|July 31, 2015
Summary
A novel convolution method improves radiation therapy planning by directly using measured beam profiles to reoptimize treatment planning system (TPS) models, effectively addressing ionization chamber volume averaging. This enhances accuracy and reduces variability in intensity-modulated radiation therapy quality assurance.
Area of Science:
- Medical Physics
- Radiation Oncology
- Computational Imaging
Background:
- The ionization chamber volume averaging effect presents a significant challenge in model-based treatment planning systems (TPSs).
- Existing methods attempt to correct for volume averaging post-measurement, often lacking elegance or comprehensive accuracy.
- Accurate beam modeling is crucial for effective and safe radiation therapy delivery.
Purpose of the Study:
- To introduce a novel convolution-based approach for addressing the volume averaging effect in TPS beam modeling.
- To reoptimize TPS beam model parameters by directly incorporating ionization chamber-measured profiles.
- To enhance the accuracy and reduce variability in radiation therapy planning.
Main Methods:
- A convolution approach was developed where TPS-calculated profiles are convolved with the detector response function.
- Beam model parameters, particularly those affecting penumbra, were iteratively optimized to match convolved profiles with measured profiles.
- The reoptimized beam model was validated using diode measurements and compared against a standard model and a clinical model in IMRT QA.
Main Results:
- The reoptimized beam model demonstrated excellent agreement with diode measurements across various geometries.
- Intensity-modulated radiation therapy (IMRT) quality assurance (QA) passing rates significantly improved, increasing from 92.1% to 99.3% (3%/3mm) and 79.2% to 95.2% (2%/2mm) compared to the standard model.
- The performance of the reoptimized model was comparable to a clinically established, manually optimized model.
Conclusions:
- The proposed convolution-based method effectively addresses the ionization chamber volume averaging effect in TPS beam modeling.
- This approach leads to improved accuracy, reduced inter-user variability, and enhanced IMRT QA performance.
- The method is easily integrated into existing model-based TPS workflows.
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