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Respiratory Motion Correction of Liver Contrast-Enhanced Ultrasound Sequences by Selecting Reference Image Automatically |
Zhang Ji1*, Zhang Yanrong2, Chen Juan2, Chen Xiaohui1, Zhong Xiaoli1 |
1Zhongnan Hospital of Wuhan University, Department of Medical Imaging, Wuhan 430070, China 2Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong Universtiy of Science and Technology, Wuhan 430070, China |
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Abstract The selection of reference image is one of the key roles for the efficiency of respiratory motion correction method based on liver contrast-enhance ultrasound (CEUS) image sequences. The selection method is worthwhile exploring. First, the original high-dimensional ultrasound data was mapped into a two-dimensional space by using Laplacian eigenmaps. Then, the cluster analysis was adopted using k-means, and the optimal ultrasound reference image was obtained for the respiratory motion correction. Finally, the effectiveness of this proposed method was validated with 18 CEUS cases of VX2 tumor implanted in the rabbit liver. Before correction, the average of total mean structural similarity and the average of mean correlation coefficient 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) as 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 of DV was reduced to one-third of the original value. The proposed respiratory motion method improved the accuracy of the quantitative analysis of CEUS by using the reference image based on the traditionally manual selection as well as operated simply, therefore is of potentials in the clinical application.
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Received: 28 July 2016
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[1] von Herbay A, Westendorff J, Gregor M. Contrast-enhanced ultrasound with SonoVue: differentiation between benign and malignant focal liver lesions in 317 patients [J]. J Clin Ultrasound, 2010, 38(1):1-9. [2] 蒋映丰,周启昌, 朱才义.超声造影在肝脏良恶性肿瘤鉴别诊断中的价值 [J].中南大学学报(医学版),2012, 37(1):53-56. [3] Zhang J, Ding M, Meng F, et al. Quantitative evaluation of two-factor analysis applied to hepatic perfusion study in contrast-enhanced ultrasound [J]. IEEE Trans Biomed Eng, 2013, 60(2):259-267. [4] 张冀,丁明跃,孟燔, 等. 基于因子分析法的超声肝灌注定量分析初步研究[J]. 中国生物医学工程学报,2011,30(3): 46-51. [5] Anaye A, Perrenoud G, Rognin N, et al. Differentiation of focal liver lesions: usefulness of parametric imaging with contrast-enhanced US [J]. Radiology, 2011, 261(1):300-310. [6] Goetti R, Reiner CS, Knuth A, et al. Quantitative perfusion analysis of malignant liver tumors: dynamic computed tomography and contrast-enhanced ultrasound [J]. Invest Radiol, 2012, 47(1):18-24. [7] Zhang J, Ding M, Meng F, et al. Respiratory motion correction in free-breathing ultrasound image sequence for quantification of hepatic perfusion [J]. Med Phys, 2011, 38(8): 4737-4748. [8] Renault G, Tranquart F, Perlbarg V, et al. A posteriori respiratory gating in contrast ultrasound for assessment of hepatic perfusion [J]. Phys Med Biol, 2005, 50 (19):4465-4480. [9] Rognin N, Arditi M, Mercier PL, et al. Parametric imaging for characterizing focal liver lesions in contrast-enhanced ultrasound [J]. IEEE Trans Ultrason Ferr, 2010, 57(11), 2503-2511. [10] Bouhlel N, Coron A,Barrois G,et al. Dual-mode registration of dynamic contrast-enhanced ultrasound combining tissue and contrast sequences[J]. Ultrasonics, 2014, 54(5):1289-1299. [11] Ta CN, Eghtedari M, Mattrey RF, et al. 2-tier in-plane motion correction and out-of-plane motion filtering for contrast-enhanced ultrasound[J]. Invest Radiol, 2014, 49(11):707-719. [12] Christofides D, Leen E, Averkiou M. Automatic respiratory gating for contrast ultrasound evaluation of liver lesions[J]. IEEE Trans Ultrason Ferr, 2014,61(1):25-32. [13] Mulé S, Kachenoura N, Lucidarme O, et al. An automatic respiratory gating method for the improvement of microcirculation evaluation: application to contrast-enhanced ultrasound studies of focal liver lesions[J]. Phys Med Biol, 2011, 56(16):5153-5165. [14] Belkin M, Niyogi P. Laplacian Eigenmaps for dimensionality reduction and data representation [J]. Neural Comput, 2003,15(6):1373-1396. [15] Liang B, Xiong F, Wu H, et al. Effect of transcatheter intraarterial therapies on the distribution of doxorubicin in liver cancer in a rabbit model[J]. PLoS ONE, 2013, 8(10):e76388. [16] Wang Z, Bovik AC, Sheikh HR, et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Trans Image Process, 2004,13(4):600-612. [17] Wu J, Gogna A, Tan BS, et al. A manifold learning method to detect respiratory signal from liver ultrasound images[J]. Comput Med Imaging Graph, 2015,40:194-204. [18] Usman M, Vaillant G, Atkinson D, et al. Compressive manifold learning: estimating one-dimensional respiratory motion directly from undersampled k-space data[J]. Magn Reson Med, 2014,72(4):1130-1140. [19] Zhang Q, Li C, Han H, et al. Computer-aided quantification of contrast agent spatial distribution within atherosclerotic plaque in contrast-enhanced ultrasound image sequences[J]. Biomed Signal Proces, 2014, 13(13): 50-61. |
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