13 March 2017 Respiratory motion correction of liver contrast-enhanced ultrasound sequences by selecting reference image automatically
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Abstract
Objective: Respiratory motion correction is necessary to quantitative analysis of liver contrast-enhance ultrasound (CEUS) image sequences. However, traditionally manual selecting reference image would affect the accuracy of the respiratory motion correction. Methods First, the original high-dimensional ultrasound gray-level image data was mapped into a two-dimensional space by using Laplacian Eigenmaps (LE). Then, the cluster analysis was adopted using K-means, and the optimal ultrasound reference image could be gotten for respiratory motion correction. Finally, this proposed method was validated on 18 CEUS cases of VX2 tumor in rabbit liver, and the effectiveness of this method was demonstrated. Results After correction, the time-intensity curves extracted from the region of interest of CEUS image sequences became smoother. Before correction, the average of total mean structural similarity (TMSSIM) and the average of mean correlation coefficient (MCC) from image sequences were 0.45±0.11 and 0.67±0.16, respectively. After correction, the two parameters were increased obviously(P<0.001), and were 0.59±0.11 and 0.81±0.11, respectively. The average of deviation valve (DV) from image sequences before correction was 92.16±18.12. After correction, the average was reduced to one-third of the original value. Conclusions: The proposed respiratory motion method could improve the accuracy of the quantitative analysis of CEUS by using the reference image based on the traditionally manual selection. This method is operated simply and has a potential in clinical application.
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Ji Zhang, Ji Zhang, Yan-Rong Zhang, Yan-Rong Zhang, Juan Chen, Juan Chen, Xiao-Hui Chen, Xiao-Hui Chen, Xiao-Li Zhong, Xiao-Li Zhong, } "Respiratory motion correction of liver contrast-enhanced ultrasound sequences by selecting reference image automatically", Proc. SPIE 10139, Medical Imaging 2017: Ultrasonic Imaging and Tomography, 101391F (13 March 2017); doi: 10.1117/12.2253658; https://doi.org/10.1117/12.2253658
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