For X-ray computed tomography (CT), geometric calibration and rigid patient motion compensation are inter-related issues for optimization of image reconstruction quality. Non-calibrated system geometry and patient movement during a CT scan will result in streak-like, blurring and other artifacts in reconstructed images. In this paper, we propose a locally linear embedding based calibration approach to address this challenge under a rigid 2D object assumption and a more general way than what has been reported before. In this method, projections are linearly represented by up-sampled neighbors via locally linear embedding, and CT system parameters are iteratively estimated from projection data themselves. Numerical and experimental studies show that images reconstructed with calibrated parameters are in excellent agreement with the counterparts reconstructed with the true parameters.
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