NDLI: Feasibility Study of Automated Framework for Estimating Lung Tumor Locations for Target-Based Patient Positioning in Stereotactic Body Radiotherapy

Content Provider	PubMed Central
Author	Yoshidome, Satoshi Arimura, Hidetaka Nakamura, Katsumasa Yoshiyuki, Shioyama Atsumi, Kazushige Nakamura, Yasuhiko Yoshikawa, Hideki Nishikawa, Kei Hirata, Hideki
Copyright Year	2015
Abstract	Objective. To investigate the feasibility of an automated framework for estimating the lung tumor locations for tumor-based patient positioning with megavolt-cone-beam computed tomography (MV-CBCT) during stereotactic body radiotherapy (SBRT). Methods. A lung screening phantom and ten lung cancer cases with solid lung tumors, who were treated with SBRT, were employed to this study. The locations of tumors in MV-CBCT images were estimated using a tumor-template matching technique between a tumor template and the MV-CBCT. Tumor templates were produced by cropping the gross tumor volume (GTV) regions, which were enhanced by a Sobel filter or a blob structure enhancement (BSE) filter. Reference tumor locations (grand truth) were determined based on a consensus between a radiation oncologist and a medical physicist. Results. According to the results of the phantom study, the average Euclidean distances of the location errors in the original, Sobel-filtered, and BSE-filtered images were 2.0 ± 4.1 mm, 12.8 ± 9.4 mm, and 0.4 ± 0.5 mm, respectively. For clinical cases, these were 3.4 ± 7.1 mm, 7.2 ± 11.6 mm, and 1.6 ± 1.2 mm, respectively. Conclusion. The feasibility study suggests that our proposed framework based on the BSE filter may be a useful tool for tumor-based patient positioning in SBRT.
Related Links	http://dx.doi.org/10.1155/2015/653974
Starting Page	653974
File Format	PDF
ISSN	23146133
e-ISSN	23146141
Journal	BioMed Research International
Volume Number	2015
Language	English
Publisher	Hindawi Publishing Corporation
Publisher Date	2015-01-01
Access Restriction	Open
Rights Holder	Hindawi Publishing Corporation
Subject Keyword	Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Immunology and Microbiology Medicine Biochemistry, Genetics and Molecular Biology

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