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

研究成果: 著書/レポートタイプへの貢献会議での発言

抄録

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.

元の言語英語
ホスト出版物のタイトルMedical Imaging 2017
ホスト出版物のサブタイトルPhysics of Medical Imaging
編集者Taly Gilat Schmidt, Joseph Y. Lo, Thomas G. Flohr
出版者SPIE
ISBN(電子版)9781510607095
DOI
出版物ステータス出版済み - 1 1 2017
イベントMedical Imaging 2017: Physics of Medical Imaging - Orlando, 米国
継続期間: 2 13 20172 16 2017

出版物シリーズ

名前Progress in Biomedical Optics and Imaging - Proceedings of SPIE
10132
ISSN(印刷物)1605-7422

その他

その他Medical Imaging 2017: Physics of Medical Imaging
米国
Orlando
期間2/13/172/16/17

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
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

これを引用

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. : T. G. Schmidt, J. Y. Lo, & T. G. Flohr (版), Medical Imaging 2017: Physics of Medical Imaging [101322F] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; 巻数 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. 版 / Taly Gilat Schmidt; Joseph Y. Lo; Thomas G. Flohr. SPIE, 2017. 101322F (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; 巻 10132).

研究成果: 著書/レポートタイプへの貢献会議での発言

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. : TG Schmidt, JY Lo & TG Flohr (版), Medical Imaging 2017: Physics of Medical Imaging., 101322F, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 巻. 10132, SPIE, Medical Imaging 2017: Physics of Medical Imaging, Orlando, 米国, 2/13/17. https://doi.org/10.1117/12.2253857
Yoshidome S, Arimura H, Terashima K, Hirakawa M, Hirose TA, Fukunaga J その他. Automated framework for estimation of lung tumor locations in kV-CBCT images for tumor-based patient positioning in stereotactic lung body radiotherapy. : Schmidt TG, Lo JY, Flohr TG, 編集者, 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. 編集者 / Taly Gilat Schmidt ; Joseph Y. Lo ; Thomas G. Flohr. SPIE, 2017. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE).
@inproceedings{30ec908fdc6442ce8baacba618e9835a,
title = "Automated framework for estimation of lung tumor locations in kV-CBCT images for tumor-based patient positioning in stereotactic lung body radiotherapy",
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.",
author = "Satoshi Yoshidome and Hidetaka Arimura and Koutarou Terashima and Masakazu Hirakawa and Hirose, {Taka Aki} and Junichi Fukunaga and Yasuhiko Nakamura",
year = "2017",
month = "1",
day = "1",
doi = "10.1117/12.2253857",
language = "English",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Schmidt, {Taly Gilat} and Lo, {Joseph Y.} and Flohr, {Thomas G.}",
booktitle = "Medical Imaging 2017",
address = "United States",

}

TY - GEN

T1 - Automated framework for estimation of lung tumor locations in kV-CBCT images for tumor-based patient positioning in stereotactic lung body radiotherapy

AU - Yoshidome, Satoshi

AU - Arimura, Hidetaka

AU - Terashima, Koutarou

AU - Hirakawa, Masakazu

AU - Hirose, Taka Aki

AU - Fukunaga, Junichi

AU - Nakamura, Yasuhiko

PY - 2017/1/1

Y1 - 2017/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85020399625&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85020399625&partnerID=8YFLogxK

U2 - 10.1117/12.2253857

DO - 10.1117/12.2253857

M3 - Conference contribution

AN - SCOPUS:85020399625

T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE

BT - Medical Imaging 2017

A2 - Schmidt, Taly Gilat

A2 - Lo, Joseph Y.

A2 - Flohr, Thomas G.

PB - SPIE

ER -