Robust Self-Supervised Learning of Deterministic Errors in Single-Plane (Monoplanar) and Dual-Plane (Biplanar) X-Ray Fluoroscopy

Abstract

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 singleplane 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-nearestneighbour (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).

Enhancing Single-Plane Fluoroscopy: A Self-Calibrating Bundle Adjustment for Distortion Modeling

“…Several methods have been successfully implemented for the geometric calibration of fluoroscopy imaging systems, including global polynomial estimation [ 16 ] and maximum likelihood estimation [ 17 ]. These methods each have their relative strengths and weaknesses; the self-calibrating bundle adjustment method presented in this study also faces unique challenges.…”

“…Recent studies have also detailed the use of self-supervised methods for the accurate modeling of systematic distortion within fluoroscopy systems. This is accomplished through the integration of iterative maximum likelihood estimation and k-nearest-neighbor regression [ 17 ].…”

Development and Validation of a Subject-Specific Coupled Model for Foot and Sports Shoe Complex: A Pilot Computational Study

“…Nevertheless, the results of rearfoot pressure comparison and Bland–Altman analysis between two methods demonstrated that the current FE model is useful under the balanced standing scenario, and subsequent validation study for the coupled model will be conducted under dynamic loading conditions. Meanwhile, it is worth noting that, with the accessibility of dual-plane fluoroscopy and high-resolution MRI, the FE model could be further validated against in vivo joint motion and soft tissue deformation data recorded by the two techniques [ 25 , 27 ].…”