Automated framework for estimation of lung tumor locations in kV-CBCT images for tumor-based patient positioning in stereotactic lung body radiotherapy

Satoshi Yoshidome; Hidetaka Arimura; Koutarou Terashima; Masakazu Hirakawa; Taka-aki Hirose; Junichi Fukunaga; Yasuhiko Nakamura

doi:10.1117/12.2253857

9 March 2017 Automated framework for estimation of lung tumor locations in kV-CBCT images for tumor-based patient positioning in stereotactic lung body radiotherapy

Satoshi Yoshidome, Hidetaka Arimura, Koutarou Terashima, Masakazu Hirakawa, Taka-aki Hirose, Junichi Fukunaga, Yasuhiko Nakamura

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Proceedings Volume 10132, Medical Imaging 2017: Physics of Medical Imaging; 101322F (2017) https://doi.org/10.1117/12.2253857
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

Recently, image-guided radiotherapy (IGRT) systems using kilovolt cone-beam computed tomography (kV-CBCT) images have become more common for highly accurate patient positioning in stereotactic lung body radiotherapy (SLBRT). However, current IGRT procedures are based on bone structures and subjective correction. Therefore, the aim of this study was to evaluate the proposed framework for automated estimation of lung tumor locations in kV-CBCT images for tumor-based patient positioning in SLBRT. Twenty clinical cases are considered, involving solid, pure ground-glass opacity (GGO), mixed GGO, solitary, and non-solitary tumor types. The proposed framework consists of four steps: (1) determination of a search region for tumor location detection in a kV-CBCT image; (2) extraction of a tumor template from a planning CT image; (3) preprocessing for tumor region enhancement (edge and tumor enhancement using a Sobel filter and a blob structure enhancement (BSE) filter, respectively); and (4) tumor location estimation based on a template-matching technique. The location errors in the original, edge-, and tumor-enhanced images were found to be 1.2 ± 0.7 mm, 4.2 ± 8.0 mm, and 2.7 ± 4.6 mm, respectively. The location errors in the original images of solid, pure GGO, mixed GGO, solitary, and non-solitary types of tumors were 1.2 ± 0.7 mm, 1.3 ± 0.9 mm, 0.4 ± 0.6 mm, 1.1 ± 0.8 mm and 1.0 ± 0.7 mm, respectively. These results suggest that the proposed framework is robust as regards automatic estimation of several types of tumor locations in kV-CBCT images for tumor-based patient positioning in SLBRT.

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Satoshi Yoshidome, Hidetaka Arimura, Koutarou Terashima, Masakazu Hirakawa, Taka-aki Hirose, Junichi Fukunaga, and Yasuhiko Nakamura "Automated framework for estimation of lung tumor locations in kV-CBCT images for tumor-based patient positioning in stereotactic lung body radiotherapy", Proc. SPIE 10132, Medical Imaging 2017: Physics of Medical Imaging, 101322F (9 March 2017); https://doi.org/10.1117/12.2253857

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KEYWORDS

Tumors

Lung

Radiotherapy

Solids

Error analysis

Image enhancement

Computed tomography

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