Cited by 19Augmented Reality ApplicationsAnatomy and Medical TechnologyMedical Image Segmentation Techniques

Towards clinical translation of augmented orthopedic surgery: from pre-op CT to intra-op x-ray via RGBD sensing

Tucker, Emerson W., Fotouhi, Javad, Lee, Sing Chun, Unberath, Mathias, Fuerst, Bernhard, Johnson, Alex, Armand, Mehran, Osgood, Greg M. +1 more·2018·Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications

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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Crossref · 10.1117/12.2293675

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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.”

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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)
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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