臨床利用におけるDIRの概要
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| Published in | 医学物理 Vol. 39; no. 1; pp. 7 - 11 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | Japanese |
| Published |
公益社団法人 日本医学物理学会
31.05.2019
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1345-5354 2186-9634 |
| DOI | 10.11323/jjmp.39.1_7 |
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| Author | 木藤, 哲史 |
|---|---|
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| Copyright | 2019 日本医学物理学会 |
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| DOI | 10.11323/jjmp.39.1_7 |
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| References | 6） Hardcastle N, van Elmpt W, De Ruysscher D, et al.: Accuracy of deformable image registration for contour propagation in adaptive lung radiotherapy. Radiat. Oncol. (London, England), 8: 243, 2013 8） Guckenberger M, Kavanagh A, Partridge M: Combining advanced radiotherapy technologies to maximize safety and tumor control probability in stage III non-small cell lung cancer. Strahlenther. Onkol. 188: 894–900, 2012 5） Thörnqvist S, Petersen JB, Høyer M, et al.: Propagation of target and organ at risk contours in radiotherapy of prostate cancer using deformable image registration, Acta. Oncol. 49: 1023–1032, 2010 10） Dong L: Online Adaptive Radiotherapy: Are We Ready? AAPM Summber School, 2011 12） Thirion JP: Image matching as a diffusion process: An analogy with Maxwell’s demons. Med. Image Anal. 2: 243–260, 1998 1） 木藤哲史，角谷倫之，藤田幸男，他：DIRの基礎とコミッショニング．第31回高精度放射線外部照射部会学術大会，2018 13） Kadoya N, Miyasaka Y, Yamamoto T, et al.: Evaluation of rectum and bladder dose accumulation from external beam radiotherapy and brachytherapy for cervical cancer using two different deformable image registration techniques. J. Radiat. Res. 1–9, 2017 3） Yang X, Wu N, Cheng G, et al.: Automated segmentation of the parotid gland based on atlas registration and machine learning: A longitudinal MRI study in head-and-neck radiation therapy. Int. J. Radiat. Oncol. Biol. Phys. 90: 1225–1233, 2014 9） Schwartz DL, Garden AS, Shah SJ et al.: Adaptive radiotherapy for head and neck cancer: Dosimetric results from a prospective clinical trial. Radiother. Oncol. 106: 80–84, 2013 4） Yang J, Amini A, Williamson R, et al.: Automatic contouring of brachial plexus using a multi-atlas approach for lung cancer radiation therapy. Pract. Radiat. Oncol. 3: e139–147, 2013 2） Brock KK, Mutic S, McNutt TR, et al.: Use of image registration and fusion algorithms and techniques in radiotherapy: Report of the AAPM Radiation Therapy Committee Task Group No. 132. Med. Phys. 44: e43–e76, 2017 7） Yamamoto T, Kabus S, Klinder T, et al.: Four-dimensional computed tomography pulmonary ventilation images vary with deformable image registration algorithms and metrics. Med. Phys. 38: 1348–1358, 2011 11） Senthi S, Griffioen GH, van Sörnsen de Koste JR, et al.: Comparing rigid and deformable dose registration for high dose thoracic re-irradiation. Radiother. Oncol. 106: 323–326, 2013 |
| References_xml | – reference: 12） Thirion JP: Image matching as a diffusion process: An analogy with Maxwell’s demons. Med. Image Anal. 2: 243–260, 1998 – reference: 7） Yamamoto T, Kabus S, Klinder T, et al.: Four-dimensional computed tomography pulmonary ventilation images vary with deformable image registration algorithms and metrics. Med. Phys. 38: 1348–1358, 2011 – reference: 3） Yang X, Wu N, Cheng G, et al.: Automated segmentation of the parotid gland based on atlas registration and machine learning: A longitudinal MRI study in head-and-neck radiation therapy. Int. J. Radiat. Oncol. Biol. Phys. 90: 1225–1233, 2014 – reference: 9） Schwartz DL, Garden AS, Shah SJ et al.: Adaptive radiotherapy for head and neck cancer: Dosimetric results from a prospective clinical trial. Radiother. Oncol. 106: 80–84, 2013 – reference: 11） Senthi S, Griffioen GH, van Sörnsen de Koste JR, et al.: Comparing rigid and deformable dose registration for high dose thoracic re-irradiation. Radiother. Oncol. 106: 323–326, 2013 – reference: 6） Hardcastle N, van Elmpt W, De Ruysscher D, et al.: Accuracy of deformable image registration for contour propagation in adaptive lung radiotherapy. Radiat. Oncol. (London, England), 8: 243, 2013 – reference: 4） Yang J, Amini A, Williamson R, et al.: Automatic contouring of brachial plexus using a multi-atlas approach for lung cancer radiation therapy. Pract. Radiat. Oncol. 3: e139–147, 2013 – reference: 13） Kadoya N, Miyasaka Y, Yamamoto T, et al.: Evaluation of rectum and bladder dose accumulation from external beam radiotherapy and brachytherapy for cervical cancer using two different deformable image registration techniques. J. Radiat. Res. 1–9, 2017 – reference: 8） Guckenberger M, Kavanagh A, Partridge M: Combining advanced radiotherapy technologies to maximize safety and tumor control probability in stage III non-small cell lung cancer. Strahlenther. Onkol. 188: 894–900, 2012 – reference: 2） Brock KK, Mutic S, McNutt TR, et al.: Use of image registration and fusion algorithms and techniques in radiotherapy: Report of the AAPM Radiation Therapy Committee Task Group No. 132. Med. Phys. 44: e43–e76, 2017 – reference: 1） 木藤哲史，角谷倫之，藤田幸男，他：DIRの基礎とコミッショニング．第31回高精度放射線外部照射部会学術大会，2018 – reference: 5） Thörnqvist S, Petersen JB, Høyer M, et al.: Propagation of target and organ at risk contours in radiotherapy of prostate cancer using deformable image registration, Acta. Oncol. 49: 1023–1032, 2010 – reference: 10） Dong L: Online Adaptive Radiotherapy: Are We Ready? AAPM Summber School, 2011 |
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| SubjectTerms | contour-based DIR deformable image registration displacement vector field hybrid-DIR intensity-based DIR |
| Title | 臨床利用におけるDIRの概要 |
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