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4D interventional device reconstruction from biplane fluoroscopy
Open accessAbstract
Purpose Biplane angiography systems provide time resolved 2D fluoroscopic images from two different angles, which can be used for the positioning of interventional devices such as guidewires and catheters. The purpose of this work is to provide a novel algorithm framework, which allows the 3D reconstruction of these curvilinear devices from the 2D projection images for each time frame. This would allow creating virtual projection images from arbitrary view angles without changing the position of the gantries, as well as virtual endoscopic 3D renderings. Methods The first frame of each time sequence is registered to and subtracted from the following frame using an elastic grid registration technique. The images are then preprocessed by a noise reduction algorithm using directional adaptive filter kernels and a ridgeness filter that emphasizes curvilinear structures. A threshold based segmentation of the device is then performed, followed by a flux driven topology preserving thinning algorithm to extract the segments of the device centerline. The exact device path is determined using Dijkstra's algorithm to minimize the curvature and distance between adjacent segments as well as the difference to the device path of the previous frame. The 3D device centerline is then reconstructed using epipolar geometry. Results The accuracy of the reconstruction was measured in a vascular head phantom as well as in a cadaver head and a canine study. The device reconstructions are compared to…
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“The registration of intraoperative 2D X-rays to 3D preoperative scans is a prerequisite for numerous advanced image guidance techniques, including vertebral level localization [11], surgical plan reprojection [12,13], instrument tracking [14], motion correction [15], and surgical robotics [16].”
From Rapid patient-specific neural networks for intraoperative X-ray to volume registration (2025) · cites this paper as [14]
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“While segmentation labels are relatively easy to derive for most structures on CT and MRI thanks to open-source tools such as TotalSegmentator [12] or MOOSE [13,14], other structures like vessels remain difficult to segment.”
From Rapid patient-specific neural networks for intraoperative X-ray to volume registration (2025) · cites this paper as [14]
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Metadata
- Authors
- Wagner, M., Schafer, S., Strother, C., & Mistretta, C.
- Affiliations
- University of Wisconsin–Madison (US)
- Corresponding author
- Wagner, Martin
- Venue
- Medical Physics
- Publisher
- Wiley
- Published
- 2016-02-19
- Volume
- 43
- Issue
- 3
- Pages
- 1324-1334
- ISSN
- 0094-2405 (ISSN-L)
- 2473-4209
- PMC
- PMC4760973
- OpenAlex
- W2285215379
- Cited by
- 48
- References
- 27
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 11 · 2023: 6 · 2022: 6 · 2021: 8 · 2020: 4 · 2019: 2 · 2018: 2 · 2016: 1
- FWCI
- 1.13
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Medical Physics (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Heart, Lung, and Blood Institute (NHLBI) (R01HL116567)
- National Heart, Lung, and Blood Institute (R01HL116567)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiation Dose and Imaging
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Cardiac Imaging and Diagnostics
- Keywords
- biplane fluoroscopy, 3D reconstruction, guidewire, catheters, epipolar geometry, Dijkstra's algorithm, virtual endoscopy, endovascular intervention
- MeSH terms
- Animals, Dogs, Fluoroscopy, Humans, Time Factors, Phantoms, Imaging, Imaging, Three-Dimensional
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- Wagner, M., Schafer, S., Strother, C., & Mistretta, C. (2016). 4D interventional device reconstruction from biplane fluoroscopy. Medical Physics. https://doi.org/10.1118/1.4941950
