2d/3d Registration of Endoscopic Ultrasound to Ct Volume Data
Abstract
This paper describes a computer-aided navigation system using image fusion to support endoscopic interventions such as the accurate collection of biopsy specimens. An endoscope provides the physician with real-time ultrasound (US) and a video image. An image slice that corresponds to the corresponding image from the US scan head is derived from a preoperative computed tomography (CT) or magnetic resonance image volume data set using oblique reformatting and displayed side by side with the US image. The position of the image acquired by the US scan head is determined by a miniaturized electromagnetic tracking system (EMTS) after calibrating the endoscope's scan head. The transformation between the patient coordinate system and the preoperative data set is calculated using a 2D/3D registration. This is achieved by calibrating an intraoperative interventional CT slice with an optical tracking system (OTS) using the same algorithm as for the US calibration. The slice is then used for 2D/3D registration with the coordinate system of the preoperative volume. The fiducial registration error (FRE) for the US calibration was 2.0 mm +/- 0.4 mm; the interventional CT FRE was 0.36 +/- 0.12 mm; and the 2D/3D registration target registration error (TRE) was 1.8 +/- 0.3 mm. The point-to-point registration between the OTS and the EMTS had an FRE of 0.9 +/- 0.4 mm. Finally, we found an overall TRE for the complete system to be 3.9 +/- 0.6 mm.
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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 Hummel et al., 2008
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References 48 · Cited by 47
- Authors
- Hummel, J., Figl, M., Bax, M., Bergmann, H., & Birkfellner, W.
- Affiliations
- Medical University of Vienna (AT)
- Stanford Medicine (US)
- Venue
- Physics in Medicine and Biology
- Publisher
- IOP Publishing
- Published
- 2008-07-24
- Volume
- 53
- Issue
- 16
- Pages
- 4303-4316
- ISSN
- 0031-9155 (ISSN-L)
- 1361-6560
- OpenAlex
- W2028388097
- Cited by
- 47
- References
- 48
- Citations by year
- 2025: 1 · 2023: 2 · 2022: 3 · 2021: 3 · 2020: 2 · 2019: 3 · 2018: 4 · 2017: 2 · 2016: 2 · 2015: 3 · 2014: 6 · 2013: 6 · 2012: 1
- FWCI
- 4.30
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Physics in Medicine and Biology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Austrian Science Fund
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Augmented Reality Applications
- Physical Sciences › Physics and Astronomy › Radiation › Advanced Radiotherapy Techniques
- Keywords
- endoscopic ultrasound, 2D-3D registration, CT image fusion, electromagnetic tracking, optical tracking, endoscopic biopsy, computer-assisted navigation, interventional CT, target registration error, fiducial registration error
- MeSH terms
- Algorithms, Artificial Intelligence, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Pattern Recognition, Automated, Sensitivity and Specificity, Subtraction Technique, Tomography, X-Ray Computed, Reproducibility of Results, Phantoms, Imaging, Endosonography, Imaging, Three-Dimensional
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Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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