Robust Self-Supervised Learning of Deterministic Errors in Single-Plane (Monoplanar) and Dual-Plane (Biplanar) X-ray Fluoroscopy
This work addresses the need for more accurate fluoroscopy data to enhance endovascular procedures for vascular surgeons and interventional radiologists, representing a strong specific gain rather than a foundational advancement.
The paper tackled the problem of improving surgical precision in fluoroscopy-guided procedures by developing a robust self-calibration algorithm for single-plane and dual-plane fluoroscopy, resulting in significant error reductions such as 3D mapping error dropping from 0.61-0.83 mm to 0.04 mm (94.2-95.7% improvement) and 2D reprojection error from 1.17-1.31 to 0.20-0.21 pixels (83.2-83.8% improvement).
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).