DeepWL: Robust EPID based Winston-Lutz analysis using deep learning,synthetic image generation and optical path-tracing |
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| Affiliation: | 1. School of Physical Sciences, University of Adelaide, Adelaide 5005, South Australia, Australia;2. Department of Medical Physics, Royal Adelaide Hospital, Adelaide 5000, South Australia, Australia;1. AOU Careggi, Medical Physics Unit, Florence, Italy;2. University of Florence-Department of Experimental and Clinical Biomedical Sciences “Mario Serio”, Florence, Italy;3. AOU Careggi, Radiotherapy Unit, Florence, Italy;4. Santa Maria Annunziata Hospital, Radiation Oncology Unit, Florence, Italy;1. Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran;2. Nuclear Medicine Research Center, Mashhad University of Medical Sciences, Mashhad, Iran;3. Department of Medical Physics, Royal Adelaide Hospital, Adelaide, SA, 5000, Australia;4. School of Physical Sciences, The University of Adelaide, Adelaide, SA, 5005, Australia;1. Department of Radiation Oncology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands;2. Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, the Netherlands;1. Département de physique, de génie physique et d’optique et Centre de recherche sur le cancer, Université Laval, Québec, Canada;2. Département de radio-oncologie et Axe Oncologie du CRCHU de Québec, CHU de Québec – Université Laval, QC, Canada;3. Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States;4. The University of Texas MD Anderson UTHealth Graduate School of Biomedical Sciences, Houston, TX, United States;1. Computer Engineering and Information Technology Department, Shiraz University of Technology, Shiraz, Iran;2. Health Technology Research Center, Shiraz University of Technology, Shiraz, Iran;3. Computer Engineering and Information Technology Department, Shiraz University of Technology, Shiraz, Iran;4. Oral and Dental Disease Research Center, Dental School, Shiraz University of Medical Sciences, Shiraz, Iran;5. Oral and Maxillofacial Radiology Department, Dental School, Shiraz University of Medical Sciences, Shiraz, Iran |
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| Abstract: | Radiation therapy requires clinical linear accelerators to be mechanically and dosimetrically calibrated to a high standard. One important quality assurance test is the Winston-Lutz test which localises the radiation isocentre of the linac.In the current work we demonstrate a novel method of analysing EPID based Winston-Lutz QA images using a deep learning model trained only on synthetic image data. In addition, we propose a novel method of generating the synthetic WL images and associated ‘ground-truth’ masks using an optical path-tracing engine to ‘fake’ mega-voltage EPID images.The model called DeepWL was trained on 1500 synthetic WL images using data augmentation techniques for 180 epochs. The model was built using Keras with a TensorFlow backend on an Intel Core i5-6500T CPU and trained in approximately 15 h. DeepWL was shown to produce ball bearing and multi-leaf collimator field segmentations with a mean dice coefficient of 0.964 and 0.994 respectively on previously unseen synthetic testing data. When DeepWL was applied to WL data measured on an EPID, the predicted mean displacements were shown to be statistically similar to the Canny Edge detection method. However, the DeepWL predictions for the ball bearing locations were shown to correlate better with manual annotations compared with the Canny edge detection algorithm.DeepWL was demonstrated to analyse Winston-Lutz images with an accuracy suitable for routine linac quality assurance with some statistical evidence that it may outperform Canny Edge detection methods in terms of segmentation robustness and the resultant displacement predictions. |
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| Keywords: | Deep learning Radiation oncology Quality assurance Synthetic data |
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