Geodesic Registration for Cervical Cancer Radiotherapy

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• 关键词：Brachytherapy ; Dose accumulation ; Cervical cancer ; Geodesic registration
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9958
• 期：1
• 页码：52-59
• 全文大小：1,470 KB
• 参考文献：1.Christensen, G.E., Carlson, B., Chao, K.S.C., Yin, P., Grigsby, P.W., Nguyen, K., Dempsey, J.F., Lerma, F.A., Bae, K.T., Vannier, M.W., et al.: Image-based dose planning of intracavitary brachytherapy: registration of serial-imaging studies using deformable anatomic templates. Int. J. Rad. Oncol. Biol. Phys. 51(1), 227–243 (2001)CrossRef
  2.Kim, H., Huq, M.S., Houser, C., Beriwal, S., Michalski, D.: Mapping of dose distribution from IMRT onto MRI-guided high dose rate brachytherapy using deformable image registration for cervical cancer treatments: preliminary study with commercially available software. J. Contemp. Brachyther. 6(2), 178–184 (2014)CrossRef
  3.Berendsen, F.F., Kotte, A.N.T.J., de Leeuw, A.A.C., Jürgenliemk-Schulz, I.M., Viergever, M.A., Pluim, J.P.W.: Registration of structurally dissimilar images in MRI-based brachytherapy. Phys. Med. Biol. 59(15), 4033 (2014)CrossRef
  4.Osorio, E.M.V., Kolkman-Deurloo, I.-K.K., Schuring-Pereira, M., Zolnay, A., Heijmen, B.J.M., Hoogeman, M.S.: Improving anatomical mapping of complexly deformed anatomy for external beam radiotherapy and brachytherapy dose accumulation in cervical cancer. Med. Phys. 42(1), 206–220 (2015)CrossRef
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  8.Klein, S., Staring, M., Murphy, K., Viergever, M., Pluim, J.P.W.: others: Elastix: a toolbox for intensity-based medical image registration. IEEE Trans. Med. Imaging 29(1), 196–205 (2010)CrossRef
  9.Roy, S., Totman, J.J., Choo, B.A.: Unified registration framework for cumulative dose assessment in cervical cancer across external beam radiotherapy and brachytherapy. Proc. SPIE Med. Imaging 2016 Image Process. 9784, 978441-1–978441-6 (2016)
  10.Gass, T., Szekely, G., Goksel, O.: Registration fusion using markov random fields. In: Ourselin, S., Modat, M. (eds.) WBIR 2014. LNCS, vol. 8545, pp. 213–222. Springer, Heidelberg (2014)
  11.Wang, Z., Bhatia, K.K., Glocker, B., Marvao, A., Dawes, T., Misawa, K., Mori, K., Rueckert, D.: Geodesic patch-based segmentation. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014, Part I. LNCS, vol. 8673, pp. 666–673. Springer, Heidelberg (2014)
  12.Ghose, S., Holloway, L., Lim, K., Chan, P., Veera, J., Vinod, S.K., Liney, G., Greer, P.B., Dowling, J.: A review of segmentation and deformable registration methods applied to adaptive cervical cancer radiation therapy treatment planning. Artif. Intell. Med. 64(2), 75–87 (2015)CrossRef
  
• 作者单位：Sharmili Roy (21)
  John J. Totman (21)
  Joseph Ng (22)
  Jeffrey Low (22)
  Bok A. Choo (22)
  
  21. A*Star-NUS Clinical Imaging Research Centre, Singapore, Singapore
  22. National University Cancer Institute, Singapore, Singapore
  
• 丛书名：Clinical Image-Based Procedures. Translational Research in Medical Imaging
• ISBN：978-3-319-46472-5
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9958

文摘

Uterus, bladder and rectum are the maximally exposed organs during cervical cancer radiotherapy and are at high risk of radiation exposure. Estimation of dose accumulation in these organs across multiple fractions of external beam radiotherapy (EBRT) and brachytherapy (BT) is extremely challenging due to structural mis-correspondences and complex anatomical deformations between the EBRT and BT images. This paper proposes a unified registration framework that aligns multiple EBRT and BT images of a patient to a single coordinate frame for a cumulative dose assessment. The proposed method transforms the radiotherapy anatomical images to their distance maps from the critical organs (uterus, bladder, and rectum) and registers the distance maps. A Markov random field model is used to fuse the resulting dense deformations and transform the anatomical image. Registration accuracy is evaluated on 42 clinical image pairs and it is shown that the proposed system outperforms existing methods in the literature.