Unberath, M. et al. The impact of machine learning on 2D/3D registration for image-guided interventions: a systematic review and perspective. Front. Robot. AI 8, 716007 (2021).
Yip, M. et al. Artificial intelligence meets medical robotics. Science 381, 141–146 (2023).
Penney, G. P. et al. A comparison of similarity measures for use in 2D/3D medical image registration. IEEE Trans. Med. Imaging 17, 586–595 (1998).
Knaan, D. & Joskowicz, L. Effective intensity-based 2D/3D rigid registration between fluoroscopic X-ray and CT. In Proc. International Conference on Medical Image Computing and Computer-Assisted Intervention (eds Ellis, R. E. & Peters, T. M.) 351–358 (Springer, 2003).
Grupp, R. B. et al. Automatic annotation of hip anatomy in fluoroscopy for robust and efficient 2D/3D registration. Int. J. Comput. Assist. Radiol. Surg. 15, 759–769 (2020).
Grimm, M., Esteban, J., Unberath, M. & Navab, N. Pose-dependent weights and domain randomization for fully automatic X-ray to CT registration. IEEE Trans. Med. Imaging 40, 2221–2232 (2021).
Mahesh, M., Ansari, A. J. & Mettler, F. A. Jr Patient exposure from radiologic and nuclear medicine procedures in the United States and worldwide: 2009–2018. Radiology 307, e221263 (2022).
Cornelis, F. H., Dzaye, O., Schoellnast, H. & Solomon, S. B. Imaging of interventional therapies in oncology: image guidance, robotics, and fusion systems. In Interventional Oncology (eds Fong, Y. et al.) 1–17 https://doi.org/10.1007/978-3-030-51192-0_19-1 (Springer, 2023).
Jhawar, B. S., Mitsis, D. & Duggal, N. Wrong-sided and wrong-level neurosurgery: a national survey. J. Neurosurg. Spine 7, 467–472 (2007).
Tonetti, J., Boudissa, M., Kerschbaumer, G. & Seurat, O. Role of 3D intraoperative imaging in orthopedic and trauma surgery. Orthop. Traumatol. Surg. Res. 106, S19–S25 (2020).
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