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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, gastrointestinaltract, etc. For both the qualitative and quantitative analysis of static anddynamic X-ray images, the data should be free of systematic biases. Besidesprecise fabrication of hardware, software-based calibration solutions arecommonly used for modelling the distortions. In this primary research study, arobust photogrammetric bundle adjustment was used to model the projectivegeometry of two fluoroscopic X-ray imaging systems. However, instead of relyingon an expert photogrammetrist's knowledge and judgement to decide on aparametric model for describing the systematic errors, a self-tuningdata-driven approach is used to model the complex non-linear distortion profileof the sensors. Quality control from the experiment showed that 0.06 mm to 0.09mm 3D reconstruction accuracy was achievable post-calibration using merely 15X-ray images. As part of the bundle adjustment, the location of the virtualfluoroscopic system relative to the target field can also be spatially resectedwith an RMSE between 3.10 mm and 3.31 mm.

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

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