Autonomous lung tumor and critical structure tracking using optical flow computation and neural network prediction
| dc.contributor.author | Teo, Peng (Troy) | |
| dc.contributor.examiningcommittee | McCurdy, Boyd (Physics and Astronomy) Venkataraman, Sankar (Physics and Astronomy) Thomas, Gabriel (Electrical and Computer Engineering) Lyn, Basil (Interdisciplinary Medicine) Meyer, Juergen (University of Washington) | en_US |
| dc.contributor.supervisor | Pistorius, Stephen (Physics and Astronomy) | en_US |
| dc.date.accessioned | 2016-10-04T19:21:35Z | |
| dc.date.available | 2016-10-04T19:21:35Z | |
| dc.date.issued | 2013 | en_US |
| dc.date.issued | 2014 | en_US |
| dc.date.issued | 2015 | en_US |
| dc.date.issued | 2012 | en_US |
| dc.degree.discipline | Physics and Astronomy | en_US |
| dc.degree.level | Doctor of Philosophy (Ph.D.) | en_US |
| dc.description.abstract | Objectives. The goal in radiotherapy is to deliver adequate radiation to the tumor volume while limiting damage to the surrounding healthy tissue. However, this goal is challenged by respiratory-induced motion. The objective of this work was to identify whether motion in electronic portal images can be tracked with an optical flow algorithm and whether a neural network can predict tumor motion. Methods. A multi-resolution optical flow algorithm that incorporates weighting based on the differences between image frames was used to automatically sample the vectors corresponding to the motion. The global motion was obtained by computing the average weighted mean from the set of vectors. The algorithm was evaluated using tumor trajectories taken from seven lung cancer patients, a 3D printed patient tumor and a virtual dynamic multi-leaf collimator (DMLC) system. The feasibility of detecting and tracking motion at the field edge was examined with a proof-of-concept implementation that included (1) an algorithm that detected local motion, and (2) a control algorithm that adapted the virtual MLC. To compensate for system latency, a generalized neural network, using both offline (treatment planning data) and online (during treatment delivery) learning, was implemented for tumor motion prediction. Results and Conclusions. The algorithm tracked the global motion of the target with an accuracy of around 0.5 mm. While the accuracy is similar to other methods, this approach does not require manual delineation of the target and can, therefore, provide real-time autonomous motion estimation during treatment. Motion at the treatment field edge was tracked with an accuracy of -0.4 ± 0.3 mm. This proof-of-concept simulation demonstrated that it is possible to adapt MLC leaves based on the motion detected at the field edges. Unplanned intrusions of external organs-at-risk could be shielded. A generalized network with a prediction error of 0.59 mm, and a shorter initial learning period (compared to previous studies) was achieved. This network may be used as a plug-and-play predictor in which tumor position could be predicted at the start of treatment and the need for pretreatment data and optimization for individual patients may be avoided. | en_US |
| dc.description.note | February 2017 | en_US |
| dc.identifier.citation | P. T. Teo, R. Crow, S. Van Nest, D. Sasaki and S. Pistorius, "Tracking lung tumour motion using a dynamically weighted optical flow algorithm and electronic portal imaging device", Measurement Science Technology, 24, 074012, 2013. | en_US |
| dc.identifier.citation | T. Teo and S. Pistorius, “Tissue motion tracking at the edges of a radiation treatment field using local optical flow analysis”, Journal of Physics: Conference Series, 489, 012040, 2014. | en_US |
| dc.identifier.citation | P. T. Teo, K, Guo, N. Alayoubi, K. Kehler and S. Pistorius, “Drift correction techniques in the tracking of lung tumor motion”, World Congress on Medical Physics and Biomedical Engineering, June 7-12, 2015, Toronto, Canada.Volume 51 of the series IFMBE Proceedings pp 575-578. | en_US |
| dc.identifier.citation | P. T. Teo, N. Bruce and S. Pistorius, “Application and parametric studies of a sliding window neural network for respiratory motion predictions of lung cancer patients”, World Congress in Medical Physics & Biomedical Engineering, Toronto, June 7-12, 2015, Toronto, Canada. Volume 51 of the series of IFMBE Proceedings, pp 595-598. | en_US |
| dc.identifier.citation | P. Teo, R..Crow, S. Van Nest, and S. Pistorius, “Tracking a phantom's lung tumour target using optical flow”, Proc. IEEE Int. Conf. Imaging Systems &Technology. (IST2012), Manchester, U.K. July 15 -17, 2012. | en_US |
| dc.identifier.uri | http://hdl.handle.net/1993/31874 | |
| dc.language.iso | eng | en_US |
| dc.publisher | IOP Publishing Ltd | en_US |
| dc.publisher | IOP Publishing Ltd | en_US |
| dc.publisher | Springer International Publishing | en_US |
| dc.publisher | IEEE | en_US |
| dc.rights | open access | en_US |
| dc.subject | Image-guided radiotherapy, Tumor tracking, Critical structure, Optical flow algorithm, Neural network | en_US |
| dc.title | Autonomous lung tumor and critical structure tracking using optical flow computation and neural network prediction | en_US |
| dc.type | doctoral thesis | en_US |