Czajkowski P, Piotrowski T. Registration methods in radiotherapy. Rep Pract Oncol Radiother. 2019;24(1):28–34. https://doi.org/10.1016/j.rpor.2018.09.004.
The Korean. J Pancreas Biliary Tract, 12(2), 221–4.
Gonzalez RC, Woods RE. Digital Image processing. 4th Ed. New York: Pearson Education; 2018.
Fu Y, Lei Y, Wang T, Curran WJ, Liu T, Yang X. Deep learning in medical image registration: a review. Phys Med Biol. 2020;65(20):20TR01. https://doi.org/10.1088/1361-6560/ab843e
Boveiri HR, Khayami R, Javidan R, Mehdizadeh A. Medical image registration using deep neural networks: a comprehensive review. Comput Electr Eng. https://doi.org/10.1016/j.compeleceng.2020.106767
Maintz JBA, Viergever MA. A survey of medical image registration, medical image analysis. Vol. 2, Issue 1, 1998, pp. 1–36, ISSN 1361–8415. https://doi.org/10.1016/S1361-8415(01)80026-8
Balakrishnan G, Zhao A, Sabuncu MR, Guttag J, Dalca AV. VoxelMorph: A learning framework for deformable medical image registration. In: IEEE transactions on medical imaging, vol. 38, no. 8, pp. 1788–1800. 2019, https://doi.org/10.1109/TMI.2019.2897538
Yipeng H, Modat M, Gibson E, Li W, Ghavami N, Bonmati E, Wang G, Bandula S, Moore CM, Emberton M, Sébastien, Ourselin JA, Noble DC, Barratt. Tom Vercauteren, Weakly-supervised convolutional neural networks for multimodal image registration, Medical Image Analysis, Volume 49, 2018, Pages 1–13, ISSN 1361–8415. https://doi.org/10.1016/j.media.2018.07.002
Cédric Hémon B, Texier H, Chourak A, Simon I, Bessières. Renaud de Crevoisier, Joël Castelli, Caroline Lafond, Anaïs Barateau, Jean-Claude Nunes, indirect deformable image registration using synthetic image generated by unsupervised deep learning. Image Vis Comput. 2024;148:0262–8856. https://doi.org/10.1016/j.imavis.2024.105143.
Ratke Alexander D, Elena H, Feline Kröninger, Kevin T, Beate, Bäumer, Christian. Deep-learning-based deformable image registration of head CT and MRI scans, Front Phys. 2023. https://doi.org/10.3389/fphy.2023.1292437
Boulanger M, Nunes JC, Chourak H, Largent A, Tahri S, Acosta O, De Crevoisier R, Lafond C, Barateau A. Deep learning methods to generate synthetic CT from MRI in radiotherapy: a literature review. Phys Med. 2021;89:265–81. Epub 2021 Aug 30. PMID: 34474325.
Jin CB, Kim H, Liu M, Jung W, Joo S, Park E, Ahn YS, Han IH, Lee JI, Cui X. Deep CT to MR synthesis using paired and unpaired data. Sens (Basel). 2019;19(10):2361. https://doi.org/10.3390/s19102361.
Hsu SH, Han Z, Leeman JE, Hu YH, Mak RH, Sudhyadhom A. Synthetic CT generation for MRI-guided adaptive radiotherapy in prostate cancer. Front Oncol. 2022;12:969463. https://doi.org/10.3389/fonc.2022.969463.
Ranjan A, Lalwani D, Misra R. GAN for synthesizing CT from T2-weighted MRI data towards MR-guided radiation treatment. MAGMA. 2022;35(3):449–57. https://doi.org/10.1007/s10334-021-00974-5.
Liu Y, Chen A, Shi H, Huang S, Zheng W, Liu Z, Zhang Q, Yang X. CT synthesis from MRI using multi-cycle GAN for head-and-neck radiation therapy. Comput Med Imaging Graph. 2021;91:101953. https://doi.org/10.1016/j.compmedimag.2021.101953.
Lei Y, Harms J, Wang T, Liu Y, Shu HK, Jani AB, Curran WJ, Mao H, Liu T, Yang X. MRI-only based synthetic CT generation using dense cycle consistent generative adversarial networks. Med Phys. 2019;46(8):3565–81. https://doi.org/10.1002/mp.13617.
Yabo Fu Y, Lei J, Zhou T, Wang DS, Yu, Jonathan J, Beitler WJ, Curran. Tian Liu, and Xiaofeng Yang Synthetic CT-aided MRI-CT image registration for head and neck radiotherapy. In: Proc. SPIE 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging, 1131728 (28 February 2020). https://doi.org/10.1117/12.2549092
McKenzie EM, Santhanam A, Ruan D, O’Connor D, Cao M, Sheng K. Multimodality image registration in the head-and-neck using a deep learning-derived synthetic CT as a Bridge. Med Phys. 2020;47(3):1094–104. https://doi.org/10.1002/mp.13976.
Zhu J-Y, Park T, Isola P, Efros AA. Unpaired image-to-image translation using cycle-consistent adversarial networks. In: 2017 IEEE international conference on computer vision (ICCV), Venice, Italy. 2017. pp. 2242–2251. https://doi.org/10.1109/ICCV.2017.244
Bourou A et al. Unpaired Image-to-image translation with limited data to reveal subtle phenotypes. 2023. https://doi.org/10.48550/arXiv.2302.08503
Ozaki S. et al. Training of deep cross-modality conversion models with a small dataset, and their application in megavoltage CT to kilovoltage CT conversion. 2021. https://doi.org/10.48550/arXiv.2107.05238
Digital Imaging. and Communication in Medicine website. https://www.dicomstandard.org/
DICOM standard Browser. https://dicom.innolitics.com/
Besl PJ, McKay ND. A method for registration of 3-D shapes. In: IEEE transactions on pattern analysis and machine intelligence, vol. 14, no. 2, pp. 239–256, 1992. https://doi.org/10.1109/34.121791
MATLAB Computer. Vision Toolbox manual (pcregistericp). https://kr.mathworks.com/help/vision/ref/pcregistericp.html
Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S et al. Generative adversarial nets. In: Advances in neural information processing systems. 2014. pp. 2672–80.
Ronneberger O, Fischer P, Brox T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. ArXiv, abs/1505.04597.
Jaderberg M, Simonyan K, Zisserman A, Kavukcuoglu K. Spatial transformer networks. ArXiv, abs/1506.02025. 2015.
Avcibas I, Sankur B, Sayood K. Statistical evaluation of image quality measures. J Electron Imaging. 2002;11(2):206–23.
Van der Weken D, Nachtegael M, Kerre EE. Image quality evaluation. In: Proc 6th Int Conf Signal Process. 2002;1:711–4.
B-spine transform in SimpleITK. https://simpleitk.org/doxygen/latest/html/classitk_1_1simple_1_1BSplineTransform.html
Comments (0)