  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% improvement). When using biplanar fluoroscopy, the
3D measurement accuracy of the system improved from 0.60 mm to 0.32 mm (47.2%
improvement).
