Modelling Errors in X-ray Fluoroscopic Imaging Systems Using Photogrammetric Bundle Adjustment With a Data-Driven Self-Calibration Approach

TL;DR

This study presents a data-driven, self-calibrating photogrammetric bundle adjustment method to model and correct systematic errors in fluoroscopic X-ray imaging systems, achieving high 3D reconstruction accuracy with minimal images.

Contribution

It introduces a novel self-tuning approach for modeling complex non-linear distortions in fluoroscopic X-ray systems without expert parametric models.

Findings

Achieved 0.06 mm to 0.09 mm 3D reconstruction accuracy post-calibration.

Spatial resection RMSE between 3.10 mm and 3.31 mm.

Effective calibration with only 15 X-ray images.

Abstract

X-ray imaging is a fundamental tool of routine clinical diagnosis. Fluoroscopic imaging can further acquire X-ray images at video frame rates, thus enabling non-invasive in-vivo motion studies of joints, gastrointestinal tract, etc. For both the qualitative and quantitative analysis of static and dynamic X-ray images, the data should be free of systematic biases. Besides precise fabrication of hardware, software-based calibration solutions are commonly used for modelling the distortions. In this primary research study, a robust photogrammetric bundle adjustment was used to model the projective geometry of two fluoroscopic X-ray imaging systems. However, instead of relying on an expert photogrammetrist's knowledge and judgement to decide on a parametric model for describing the systematic errors, a self-tuning data-driven approach is used to model the complex non-linear distortion profile of the sensors. Quality control from the experiment showed that 0.06 mm to 0.09 mm 3D reconstruction accuracy was achievable post-calibration using merely 15 X-ray images. As part of the bundle adjustment, the location of the virtual fluoroscopic system relative to the target field can also be spatially resected with an RMSE between 3.10 mm and 3.31 mm.