TY - GEN
T1 - Motion artifact reduction in 4D helical CT
T2 - Workshop on Medical Computer Vision, MCV 2010, Held in Conjunction with the 13th International Conference on Medical Image Computing and Computer - Assisted Intervention, MICCAI 2010
AU - Han, Dongfeng
AU - Bayouth, John
AU - Bhatia, Sudershan
AU - Sonka, Milan
AU - Wu, Xiaodong
N1 - Funding Information:
This research was supported in part by the NSF grants CCF-0830402 and CCF-0844765, and the NIH grants R01 EB004640 and K25 CA123112.
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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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U2 - 10.1007/978-3-642-18421-5_7
DO - 10.1007/978-3-642-18421-5_7
M3 - Conference contribution
AN - SCOPUS:79951631676
SN - 9783642184208
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 63
EP - 73
BT - Medical Computer Vision
Y2 - 20 September 2010 through 20 September 2010
ER -