Regression model-based real-time markerless tumor tracking with fluoroscopic images for hepatocellular carcinoma |
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| Affiliation: | 1. Corporate Research and Development Center, Toshiba Corporation, Kanagawa 212 8582, Japan;2. Research Center for Charged Particle Therapy, National Institute of Radiological Sciences, Chiba 263 8555, Japan;1. Medical Physics Department, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Torino, Italy;2. Institute of Radiological Sciences, University of Sassari, Italy;3. Radiotherapy Department, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Torino, Italy;4. Radiotherapy, Centro Aktis, Marano di Napoli, Italy;5. U.O. Unità Fegato, A.O. Moscati, Avellino, Italy;6. Radiation Oncology Department, University Hospital, Verona, Italy;7. Radiotherapy Department, S. Camillo-Forlanini, Roma, Italy;8. Radiotherapy Department, Oncologic Businco Hospital, Cagliari, Italy;9. Hyperthermia Service, Centro Medico Serena, Padova, Italy;1. Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul 06351, Republic of Korea;2. Department of Radiation Oncology, Samsung Medical Centre, Seoul 06351, Republic of Korea;1. Department of Radiation Oncology, Cancer Center Amsterdam, VU University Medical Center, Amsterdam, The Netherlands;2. Varian Medical Systems, Palo Alto, CA, USA;1. Gunma University Heavy Ion Medical Center, Japan;2. Kanagawa Cancer Center, Japan;3. Department of Radiology, Gunma University Hospital, Japan;1. Department of Data Analysis and Modeling in Medicine, Mannheim Institute for Intelligent Systems in Medicine (MIISM), Medical Faculty of Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany;2. Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Im Neuenheimer Feld 205, 69120 Heidelberg, Germany;3. Central Institute for Computer Engineering (ZITI), Heidelberg University, Im Neuenheimer Feld 368, 69120 Heidelberg, Germany |
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| Abstract: | PurposeWe have developed a new method to track tumor position using fluoroscopic images, and evaluated it using hepatocellular carcinoma case data.MethodsOur method consists of a training stage and a tracking stage. In the training stage, the model data for the positional relationship between the diaphragm and the tumor are calculated using four-dimensional computed tomography (4DCT) data. The diaphragm is detected along a straight line, which was chosen to avoid 4DCT artifact. In the tracking stage, the tumor position on the fluoroscopic images is calculated by applying the model to the diaphragm. Using data from seven liver cases, we evaluated four metrics: diaphragm edge detection error, modeling error, patient setup error, and tumor tracking error. We measured tumor tracking error for the 15 fluoroscopic sequences from the cases and recorded the computation time.ResultsThe mean positional error in diaphragm tracking was 0.57 ± 0.62 mm. The mean positional error in tumor tracking in three-dimensional (3D) space was 0.63 ± 0.30 mm by modeling error, and 0.81–2.37 mm with 1–2 mm setup error. The mean positional error in tumor tracking in the fluoroscopy sequences was 1.30 ± 0.54 mm and the mean computation time was 69.0 ± 4.6 ms and 23.2 ± 1.3 ms per frame for the training and tracking stages, respectively.ConclusionsOur markerless tracking method successfully estimated tumor positions. We believe our results will be useful in increasing treatment accuracy for liver cases. |
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| Keywords: | Markerless tumor tracking Regression model Particle beam therapy Image guidance |
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