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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationMedical Imaging 2017
Subtitle of host publicationPhysics of Medical Imaging
EditorsTaly Gilat Schmidt, Joseph Y. Lo, Thomas G. Flohr
PublisherSPIE
ISBN (Electronic)9781510607095
DOIs
Publication statusPublished - Jan 1 2017
EventMedical Imaging 2017: Physics of Medical Imaging - Orlando, United States
Duration: Feb 13 2017Feb 16 2017

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10132
ISSN (Print)1605-7422

Other

OtherMedical Imaging 2017: Physics of Medical Imaging
CountryUnited States
CityOrlando
Period2/13/172/16/17

Fingerprint

Patient Positioning
Cone-Beam Computed Tomography
Radiosurgery
Radiotherapy
lungs
positioning
Tomography
Cones
Tumors
radiation therapy
cones
tumors
tomography
Lung
Neoplasms
Opacity
opacity
Glass
Image-Guided Radiotherapy
glass

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

Cite this

Yoshidome, S., Arimura, H., Terashima, K., Hirakawa, M., Hirose, T. A., Fukunaga, J., & Nakamura, Y. (2017). Automated framework for estimation of lung tumor locations in kV-CBCT images for tumor-based patient positioning in stereotactic lung body radiotherapy. In T. G. Schmidt, J. Y. Lo, & T. G. Flohr (Eds.), Medical Imaging 2017: Physics of Medical Imaging [101322F] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10132). SPIE. https://doi.org/10.1117/12.2253857

Automated framework for estimation of lung tumor locations in kV-CBCT images for tumor-based patient positioning in stereotactic lung body radiotherapy. / Yoshidome, Satoshi; Arimura, Hidetaka; Terashima, Koutarou; Hirakawa, Masakazu; Hirose, Taka Aki; Fukunaga, Junichi; Nakamura, Yasuhiko.

Medical Imaging 2017: Physics of Medical Imaging. ed. / Taly Gilat Schmidt; Joseph Y. Lo; Thomas G. Flohr. SPIE, 2017. 101322F (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10132).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Yoshidome, S, Arimura, H, Terashima, K, Hirakawa, M, Hirose, TA, Fukunaga, J & Nakamura, Y 2017, Automated framework for estimation of lung tumor locations in kV-CBCT images for tumor-based patient positioning in stereotactic lung body radiotherapy. in TG Schmidt, JY Lo & TG Flohr (eds), Medical Imaging 2017: Physics of Medical Imaging., 101322F, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 10132, SPIE, Medical Imaging 2017: Physics of Medical Imaging, Orlando, United States, 2/13/17. https://doi.org/10.1117/12.2253857
Yoshidome S, Arimura H, Terashima K, Hirakawa M, Hirose TA, Fukunaga J et al. Automated framework for estimation of lung tumor locations in kV-CBCT images for tumor-based patient positioning in stereotactic lung body radiotherapy. In Schmidt TG, Lo JY, Flohr TG, editors, Medical Imaging 2017: Physics of Medical Imaging. SPIE. 2017. 101322F. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE). https://doi.org/10.1117/12.2253857
Yoshidome, Satoshi ; Arimura, Hidetaka ; Terashima, Koutarou ; Hirakawa, Masakazu ; Hirose, Taka Aki ; Fukunaga, Junichi ; Nakamura, Yasuhiko. / Automated framework for estimation of lung tumor locations in kV-CBCT images for tumor-based patient positioning in stereotactic lung body radiotherapy. Medical Imaging 2017: Physics of Medical Imaging. editor / Taly Gilat Schmidt ; Joseph Y. Lo ; Thomas G. Flohr. SPIE, 2017. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE).
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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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