Automated MV markerless tumor tracking for VMAT
| Autor: | Marios Myronakis, Thomas C. Harris, I Valencia Lozano, Mathias Lehmann, M. Jacobson, Daniel Morf, Dianne Ferguson, Ross Berbeco, Rony Fueglistaller, Pascal Huber, Mengying Shi, Christopher S. Williams, Paul Baturin |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Lung Neoplasms
Computer science medicine.medical_treatment Movement Stability (learning theory) ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Image processing Radiation Tracking (particle physics) Imaging phantom Article 030218 nuclear medicine & medical imaging 03 medical and health sciences Automation 0302 clinical medicine medicine Image Processing Computer-Assisted Humans Radiology Nuclear Medicine and imaging Computer vision Radiological and Ultrasound Technology business.industry Phantoms Imaging Template matching Radiation therapy 030220 oncology & carcinogenesis Lung tumor Artificial intelligence Radiotherapy Intensity-Modulated business Algorithms Radiotherapy Image-Guided |
| Zdroj: | Phys Med Biol |
| Popis: | Tumor tracking during radiotherapy treatment can improve dose accuracy, conformity and sparing of healthy tissue. Many methods have been introduced to tackle this challenge utilizing multiple imaging modalities, including a template matching based approach using the megavoltage (MV) on-board portal imager demonstrated on 3D conformal treatments. However, the complexity of treatments is evolving with the introduction of VMAT and IMRT, and successful motion management is becoming more important due to a trend towards hypofractionation. We have developed a markerless lung tumor tracking algorithm, utilizing the electronic portal imager (EPID) of the treatment machine. The algorithm has been specifically adapted to track during complex treatment deliveries with gantry and MLC motion. The core of the algorithm is an adaptive template matching method that relies on template stability metrics and local relative orientations to perform multiple feature tracking simultaneously. Only a single image is required to initialize the algorithm and features are automatically added, modified or removed in response to the input images. This algorithm was evaluated against images collected during VMAT arcs of a dynamic thorax phantom. Dynamic phantom images were collected during radiation delivery for multiple lung SBRT breathing traces and an example patient data set. The tracking error was 1.34 mm for the phantom data and 0.68 mm for the patient data. A multi-region, markerless tracking algorithm has been developed, capable of tracking multiple features simultaneously without requiring any other a priori information. This novel approach delivers robust target localization during complex treatment delivery. The reported tracking error is similar to previous reports for 3D conformal treatments. |
| Databáze: | OpenAIRE |
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