Abstract
Four dimensional CT (4D CT) provides a way to reduce positional uncertainties caused by respiratory motion. Due to the inconsistencies of patient’s breathing, images from different respiratory periods may be misaligned, thus the acquired 3D data may not accurately represent the anatomy. In this paper, we propose a method based on graph algorithms to reduce the magnitude of artifacts present in helical 4D CT images. The method strives to reduce the magnitude of artifacts directly from the reconstructed images. The experiments on simulated data showed that the proposed method reduced the landmarks distance errors from 2.7 mm to 1.5 mm, outperforming the registration methods by about 42%. For clinical 4D CT image data, the image quality was evaluated by the three medical experts and both of who identified much fewer artifacts from the resulting images by our method than from those by the commercial 4D CT software.
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Han, D., Bayouth, J., Bhatia, S., Sonka, M., Wu, X. (2011). Motion Artifact Reduction in 4D Helical CT: Graph-Based Structure Alignment. In: Menze, B., Langs, G., Tu, Z., Criminisi, A. (eds) Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging. MCV 2010. Lecture Notes in Computer Science, vol 6533. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18421-5_7
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DOI: https://doi.org/10.1007/978-3-642-18421-5_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-18420-8
Online ISBN: 978-3-642-18421-5
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