Abstract
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.
Highlights
X-ray fluoroscopy is a valuable diagnostic imaging modality in gastroenterology, radiology, orthopaedics and many other medical specialities
To estimate a unique set of calibration parameters for a dual fluoroscopic X-ray imaging system, Lichti et al (2015) demonstrated the use of the photogrammetric bundle adjustment method to combine image space information extracted from 300 images
Fluoroscopic imaging systems allow the use of low dose radiation to look under the skin of humans non-invasively for clinical diagnoses
Summary
X-ray fluoroscopy is a valuable diagnostic imaging modality in gastroenterology, radiology, orthopaedics and many other medical specialities. Direct Linear Transformation (DLT) type methods are often used for calibrating fluoroscopic imaging systems because of their computational simplicity (You et al, 2001). It assumes that the systematic errors in every image frame are independent of the other image frames acquired by the same sensor. By considering a set of images concurrently in the distortion modelling process, the accuracy and robustness of the calibration can be improved. The authors reported achieving up to 71% improvement in 3D reconstruction accuracy after calibration This method was further extended in Al-Durgham et al (2016) where a semiautomatic target extraction and matching function was added to make the entire calibration process more efficient and userfriendly. A data-driven approach is proposed to help make the calibration procedure operator-independent by automatically selecting the most appropriate distortion profile based on the input data during bundle adjustment
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