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Modelling Errors in X-ray Fluoroscopic Imaging Systems Using Photogrammetric Bundle Adjustment With a Data-Driven Self-Calibration Approach
X-ray imaging is a fundamental tool of routine clinical diagnosis. Fluor...
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Robot Vision: Calibration of Wide-Angle Lens Cameras Using Collinearity Condition and K-Nearest Neighbour Regression
Visual perception is regularly used by humans and robots for navigation....
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i3PosNet: Instrument Pose Estimation from X-Ray
Performing delicate Minimally Invasive Surgeries (MIS) forces surgeons t...
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Pelvis Surface Estimation From Partial CT for Computer-Aided Pelvic Osteotomies
Computer-aided surgical systems commonly use preoperative CT scans when ...
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On the variance of radio interferometric calibration solutions: Quality-based Weighting Schemes
SKA-era radio interferometric data volumes are expected to be such that ...
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Self-Supervised Surgical Tool Segmentation using Kinematic Information
Surgical tool segmentation in endoscopic images is the first step toward...
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Pose Estimation of Periacetabular Osteotomy Fragments with Intraoperative X-Ray Navigation
Objective: State of the art navigation systems for pelvic osteotomies us...
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Robust Self-Supervised Learning of Deterministic Errors in Single-Plane (Monoplanar) and Dual-Plane (Biplanar) X-ray Fluoroscopy
Fluoroscopic imaging that captures X-ray images at video framerates is advantageous for guiding catheter insertions by vascular surgeons and interventional radiologists. Visualizing the dynamical movements non-invasively allows complex surgical procedures to be performed with less trauma to the patient. To improve surgical precision, endovascular procedures can benefit from more accurate fluoroscopy data via calibration. This paper presents a robust self-calibration algorithm suitable for single-plane and dual-plane fluoroscopy. A three-dimensional (3D) target field was imaged by the fluoroscope in a strong geometric network configuration. The unknown 3D positions of targets and the fluoroscope pose were estimated simultaneously by maximizing the likelihood of the Student-t probability distribution function. A smoothed k-nearest neighbour (kNN) regression is then used to model the deterministic component of the image reprojection error of the robust bundle adjustment. The Maximum Likelihood Estimation step and the kNN regression step are then repeated iteratively until convergence. Four different error modeling schemes were compared while varying the quantity of training images. It was found that using a smoothed kNN regression can automatically model the systematic errors in fluoroscopy with similar accuracy as a human expert using a small training dataset. When all training images were used, the 3D mapping error was reduced from 0.61-0.83 mm to 0.04 mm post-calibration (94.2-95.7 improvement), and the 2D reprojection error was reduced from 1.17-1.31 to 0.20-0.21 pixels (83.2-83.8 3D measurement accuracy of the system improved from 0.60 mm to 0.32 mm (47.2 improvement).
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