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16 March 2020 C-arm CT imaging using the extended line-ellipse-line trajectory: seamless FBP reconstruction from real data
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Abstract
We present further progress on the implementation of C-arm CT imaging with the extended line-ellipse-line (LEL) trajectory. This novel data acquisition geometry is designed to enhance image quality in interventional radiology. Previously, we showed that robust extended LEL data acquisition is feasible using a state-of-the-art multi-axis robotic C-arm (ARTIS pheno, Siemens Healthcare GmbH, Germany) and we also showed that accurate reconstruction from real data can be obtained using an iterative algorithm. The extensive computational effort required by such an algorithm is however not suitable for clinical translation. Reconstruction using a filtered- backprojection (FBP) formula would be practical. To use such a formula, there needs to be a technique to handle imperfections in the data acquisition geometry, which result from mechanical vibrations and gravity effects. We recently presented such a technique, but this development was only carried out for a single cycle of the LEL trajectory. In this work, we address the more challenging issue of reconstructing the volume covered by multiple cycles of the trajectory. Specifically, we propose an extension of our single cycle approach to multiple cycles. We successfully demonstrate that our procedure now allows seamless volume reconstruction from real data using a cone-beam performance phantom as well as an anthropomorphic head phantom. Our results bring the extended LEL trajectory closer to clinical deployment for improved image quality in interventional radiology. Further work will focus on increasing the number of views to avoid few view artifacts and on thoroughly demonstrating image quality benefits.
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Zijia Guo, Günter Lauritsch, Andreas Maier, and Frédéric Noo "C-arm CT imaging using the extended line-ellipse-line trajectory: seamless FBP reconstruction from real data", Proc. SPIE 11312, Medical Imaging 2020: Physics of Medical Imaging, 1131203 (16 March 2020); https://doi.org/10.1117/12.2549999
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