Abstract
We propose LiftReg, a 2D/3D deformable registration approach. LiftReg is a deep registration framework which is trained using sets of digitally reconstructed radiographs (DRR) and computed tomography (CT) image pairs. By using simulated training data, LiftReg can use a high-quality CT-CT image similarity measure, which helps the network to learn a high-quality deformation space. To further improve registration quality and to address the inherent depth ambiguities of very limited angle acquisitions, we propose to use features extracted from the backprojected 2D images and a statistical deformation model. We test our approach on the DirLab lung registration dataset and show that it outperforms an existing learning-based pairwise registration approach.
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References
Castillo, R., et al.: A reference dataset for deformable image registration spatial accuracy evaluation using the copdgene study archive. Phys. Med. Biol. 58(9), 2861 (2013)
Flach, B., Brehm, M., Sawall, S., Kachelrieß, M.: Deformable 3D–2D registration for CT and its application to low dose tomographic fluoroscopy. Phys. Med. Biol. 59(24), 7865 (2014)
Foote, M.D., Zimmerman, B.E., Sawant, A., Joshi, S.C.: Real-time 2D-3D deformable registration with deep learning and application to lung radiotherapy targeting. In: Chung, A.C.S., Gee, J.C., Yushkevich, P.A., Bao, S. (eds.) IPMI 2019. LNCS, vol. 11492, pp. 265–276. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20351-1_20
Jaderberg, M., Simonyan, K., Zisserman, A., et al.: Spatial transformer networks. In: Advances in Neural Information Processing Systems, vol. 28, pp. 2017–2025 (2015)
Jaffray, D., Kupelian, P., Djemil, T., Macklis, R.M.: Review of image-guided radiation therapy. Expert Rev. Anticancer Ther. 7(1), 89–103 (2007)
Kalender, W.A.: X-ray computed tomography. Phys. Med. Biol. 51(13), R29 (2006)
Li, P., Pei, Y., Guo, Y., Ma, G., Xu, T., Zha, H.: Non-rigid 2D–3D registration using convolutional autoencoders. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 700–704. IEEE (2020)
Markelj, P., Tomaževič, D., Likar, B., Pernuš, F.: A review of 3D/2D registration methods for image-guided interventions. Med. Image Anal. 16(3), 642–661 (2012)
Modersitzki, J.: Numerical Methods for Image Registration. OUP, Oxford (2003)
Pei, Y., et al.: Non-rigid craniofacial 2D-3D registration using CNN-based regression. In: Cardoso, M.J., et al. (eds.) DLMIA/ML-CDS -2017. LNCS, vol. 10553, pp. 117–125. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67558-9_14
Prümmer, M., Hornegger, J., Pfister, M., Dörfler, A.: Multi-modal 2D–3D non-rigid registration. In: Medical Imaging 2006: Image Processing, vol. 6144, p. 61440X. International Society for Optics and Photonics (2006)
Regan, E.A., et al.: Genetic epidemiology of COPD (COPDGene) study design. COPD J. Chronic Obstr. Pulm. Dis. 7(1), 32–43 (2011)
Shan, J., et al.: Stationary chest tomosynthesis using a carbon nanotube x-ray source array: a feasibility study. Phys. Med. Biol. 60, 81–100 (2015). https://doi.org/10.1088/0031-9155/60/1/81
Sherouse, G.W., Novins, K., Chaney, E.L.: Computation of digitally reconstructed radiographs for use in radiotherapy treatment design. Int. J. Radiat. Oncol. Biol. Phys. 18(3), 651–658 (1990)
Staub, D., Murphy, M.J.: A digitally reconstructed radiograph algorithm calculated from first principles. Med. Phys. 40(1), 011902 (2013)
Tian, L., et al.: Fluid registration between lung CT and stationary chest tomosynthesis images. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12263, pp. 307–317. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59716-0_30
Zhang, Y.: An unsupervised 2D–3D deformable registration network (2D3D-RegNet) for cone-beam CT estimation. Phys. Med. Biol. 66(7), 074001 (2021)
Zhang, Y., Tehrani, J.N., Wang, J.: A biomechanical modeling guided CBCT estimation technique. IEEE Trans. Med. Imaging 36(2), 641–652 (2016)
Zikic, D., Groher, M., Khamene, A., Navab, N.: Deformable registration of 3D vessel structures to a single projection image. In: Medical Imaging 2008: Image Processing, vol. 6914, p. 691412. International Society for Optics and Photonics (2008)
Acknowledgement
Thanks to Peirong Liu (UNC), Dr. Rong Yuan (Peking University), and Boqi Chen (UNC) for providing valuable suggestions on during the writing of the manuscript. The research reported in this publication was supported by the National Institutes of Health (NIH) under award numbers NIH 1 R01 HL149877 and NIH 1 R01 EB028283. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
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Tian, L., Lee, Y.Z., San José Estépar, R., Niethammer, M. (2022). LiftReg: Limited Angle 2D/3D Deformable Registration. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13436. Springer, Cham. https://doi.org/10.1007/978-3-031-16446-0_20
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