Abstract
Objective. Small-field dosimetry is an ongoing challenge in radiotherapy quality assurance (QA) especially for radiosurgery systems such as CyberKnife TM. The objective of this work is to demonstrate the use of a plastic scintillator imaged with a commercial camera to measure the output factor of a CyberKnife system. The output factor describes the dose on the central axis as a function of collimator size, and is a fundamental part of CyberKnife QA and integral to the data used in the treatment planning system.
Approach. A self-contained device consisting of a solid plastic scintillator and a camera was build in a portable Pelicase. Photographs were analysed using classical methods and with convolutional neural networks (CNN) to predict beam parameters which were then compared to measurements.
Main results. Initial results using classical image processing to determine standard QA parameters such as percentage depth dose (PDD) were unsuccessful, with 34% of points failing to meet the Gamma criterion (which measures the distance between corresponding points and the relative difference in dose) of 2 mm/2%. However, when images were processed using a CNN trained on simulated data and a green scintillator sheet, 92% of PDD curves agreed with measurements with a microdiamond detector to within 2 mm/2% and 78% to 1%/1 mm. The mean difference between the output factors measured using this system and a microdiamond detector was 1.1%. Confidence in the results was enhanced by using the algorithm to predict the known collimator sizes from the photographs which it was able to do with an accuracy of less than 1 mm.
Significance. With refinement, a full output factor curve could be measured in less than an hour, offering a new approach for rapid, convenient small-field dosimetry.
Original language | English |
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Article number | 025024 |
Number of pages | 12 |
Journal | Physics in Medicine and Biology |
Volume | 69 |
Issue number | 2 |
DOIs | |
Publication status | Published - 22 Jan 2024 |
Bibliographical note
Acknowledgments:JO was funded by the STFC UCL Centre for Doctoral Training in Data Intensive Science (grant no. ST/ P006736/1) and the UKRI COVID-19 Grant Extension Allocation. TS is funded by a grant from the Royal Society.
We would like to thank the CyberKnife and radiotherapy physics dosimetry team at University Hospitals Birmingham NHS Foundation Trust, Derek Attree and Connor Godden at UCL Physics and Astronomy for constructing the pelicase and Dr Edward Edmondson for help with managing the HEP computing clusters.
Keywords
- Deep Learning
- Radiometry/methods
- Radiosurgery/methods
- Algorithms
- Neural Networks, Computer