Proceedings Article | 9 March 2017
Satoshi Yoshidome, Hidetaka Arimura, Koutarou Terashima, Masakazu Hirakawa, Taka-aki Hirose, Junichi Fukunaga, Yasuhiko Nakamura
KEYWORDS: Radiotherapy, Lung, Image enhancement, Computed tomography, Solids, Image filtering, Computing systems, Bone, Opacity, Feature extraction, Tumors, Error analysis
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.
