Abstract
We present a novel and clinically useful method to automatically determine the regions that carry cardiac and respiratory motion information directly from standard mono-plane X-ray fluoroscopy images. We demonstrate the application of our method for the purposes of retrospective cardiac and respiratory gating of X-ray images. Validation is performed on five mono-plane imaging sequences comprising a total of 284 frames from five patients undergoing radiofrequency ablation for the treatment of atrial fibrillation. We established end-inspiration, end-expiration and systolic gating with success rates of 100%, 100% and 95.3%, respectively. This technique is useful for retrospective gating of X-ray images and, unlike many previously proposed techniques, does not require specific catheters to be visible and works without any knowledge of catheter geometry.
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Panayiotou, M. et al. (2014). Extraction of Cardiac and Respiratory Motion Information from Cardiac X-Ray Fluoroscopy Images Using Hierarchical Manifold Learning. In: Camara, O., Mansi, T., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds) Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges. STACOM 2013. Lecture Notes in Computer Science, vol 8330. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54268-8_15
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DOI: https://doi.org/10.1007/978-3-642-54268-8_15
Publisher Name: Springer, Berlin, Heidelberg
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