Towards clinical translation of augmented orthopedic surgery: from pre-op CT to intra-op x-ray via RGBD sensing
Abstract
Pre-operative CT data is available for several orthopedic and trauma interventions, and is mainly used to identify injuries and plan the surgical procedure. In this work we propose an intuitive augmented reality environment allowing visualization of pre-operative data during the intervention, with an overlay of the optical information from the surgical site. The pre-operative CT volume is first registered to the patient by acquiring a single C-arm X-ray image and using 3D/2D intensity-based registration. Next, we use an RGBD sensor on the C-arm to fuse the optical information of the surgical site with patient pre-operative medical data and provide an augmented reality environment. The 3D/2D registration of the pre- and intra-operative data allows us to maintain a correct visualization each time the C-arm is repositioned or the patient moves. An overall mean target registration error (mTRE) and standard deviation of 5.24 ± 3.09 mm was measured averaged over 19 C-arm poses. The proposed solution enables the surgeon to visualize pre-operative data overlaid with information from the surgical site (e.g. surgeon’s hands, surgical tools, etc.) for any C-arm pose, and negates issues of line-of-sight and long setup times, which are present in commercially available systems.
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Image-based navigation techniques as well as visual servoing of surgical robots will also require high-precision 2D/3D registration at video frame-rates.
“This is because image-based navigation techniques (Sugano, 2003; Hummel et al., 2008; Tucker et al., 2018) as well as visual servoing of surgical robots (Yi et al., 2018; Gao et al., 2019; Unberath et al., 2019) will also require high-precision 2D/3D registration at video frame-rates.”
From The Impact of Machine Learning on 2D/3D Registration for Image-Guided Interventions: A Systematic Review and Perspective (2021) · cites this paper as Tucker et al., 2018
Cites
Marker‐free motion correction in weight‐bearing cone‐beam CT of the knee joint
Berger, M., Müller, K., Aichert, A., Unberath, M., Thies, J., Choi, J. ‐., Fahrig, R., & Maier, A. (2016) · Medical Physics · doi:10.1118/1.4941012 · Cited by 72 · see citing works
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Unberath, M., Gao, C., Hu, Y., Judish, M., Taylor, R. H., Armand, M., & Grupp, R. (2021) · Frontiers in Robotics and AI · doi:10.3389/frobt.2021.716007 · Cited by 102 · see citing works
“This is because image-based navigation techniques [86, 13, 87] as well as visual servoing of surgical robots [88, 89, 90] will also require high-precision 2D/3D registration at video frame-rates.”
BackgroundSelf-citationLearning to detect anatomical landmarks of the pelvis in X-rays from arbitrary views
Bier, B., Goldmann, F., Zaech, J.-N., Fotouhi, J., Hegeman, R., Grupp, R., Armand, M., Osgood, G., Navab, N., Maier, A., et al. (2019) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-019-01975-5 · Cited by 43 · see citing works
“These solutions rely on external markers to track the tools or the patient in 3D [17], consistency conditions to estimate relative pose between Xray images [1], or 2D/3D registration of pre-operative CT to intra-operative X-ray to render multiple views simultaneously [17,25].”
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- Authors
- Tucker, E. W., Fotouhi, J., Lee, S. C., Unberath, M., Fuerst, B., Johnson, A., Armand, M., Osgood, G. M., & Navab, N.
- Affiliations
- Johns Hopkins University (US)
- Johns Hopkins Hospital (US)
- Johns Hopkins University Applied Physics Laboratory (US)
- ORCID
- Venue
- Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications
- Publisher
- SPIE
- Published
- 2018-03-06
- Pages
- 15
- OpenAlex
- W2791064202
- Cited by
- 19
- References
- 1
- Citations by year
- 2024: 1 · 2023: 1 · 2021: 4 · 2020: 3 · 2019: 3 · 2018: 7
- FWCI
- 1.28
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- SPIE Medical Imaging: Imaging Informatics for Healthcare, Research, and Applications (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Augmented Reality Applications
- Physical Sciences › Engineering › Biomedical Engineering › Anatomy and Medical Technology
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- augmented reality surgery, orthopedic surgery, 2D-3D registration, intensity-based registration, surgical navigation, target registration error
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