期刊文献+

IVUS融合冠状动脉CAG三维重建模型的角度校正研究 被引量:1

Angle Correction of IVUS on CAG 3D Reconstruction of Coronary Artery
下载PDF
导出
摘要 针对冠状动脉三维重建中IVUS采集角度偏差导致模型结果失真,提出一种在融合过程中校正IVUS融合角度的新方法。首先,分析CAG和IVUS图像中冠状动脉血管的径向信息差异计算出IVUS帧在成像过程中超声机械探头的偏移角度。其次,应用Active Demons算法判断IVUS帧在融合三维模型中的朝向。最后,将角度校正后的IVUS图像数据融合至三维骨架模型当中,完成两种数据的融合。实验表明,本文方法能大幅度改善因IVUS角度偏差而导致的IVUS图像在三维模型中的失真情况,使冠状动脉三维重建结果满足临床应用的需要。 In order to solve the IVUS distortion problem caused by the catheter rotation on the 3D reconstruction of coronary artery,a novel method was propased to correct the fusion angle of IVUS. The method starts with the analysis of radial informational differences between CAG and IVUS,and the angle of the catheter twist was obtained. Then,the direction between the IVUS and the cross-section of the coronary artery is analyzed by applying the Active Demons algorithm. Finally,the IVUS frames are settled in an ideal position to the three-dimensional model of the coronary artery and the fusion of two types of data is implemented. The experimental result shows that the method could sharply relief the anamorphose on account of IVUS distortion in 3D reconstruction model,and improved the accuracy of the 3D reconstruction of coronary artery.
出处 《科学技术与工程》 北大核心 2015年第36期84-90,共7页 Science Technology and Engineering
基金 国家自然科学基金(61271155)资助
关键词 三维重建 心血管内超声 角度校正 ACTIVE DEMONS 3D reconstruction IVUS angle correction Active Demons
  • 相关文献

参考文献16

  • 1Zheng S, Mengchan L. Reconstruction of coronary vessels from intra- vascular ultrasound image sequences based on compensation of the in- plane motion. Computerized Medical Imaging and Graphics, 2013; 37(7) : 618-627.
  • 2左明雪.人体解剖生理学.北京:高等教育出版社,2009:30-44.
  • 3毛海群,杨丰,林慕丹,黄铮,崔凯,王欣昕.基于流形学习的血管内超声图像序列关键帧的提取及应用[J].南方医科大学学报,2015,35(4):492-498. 被引量:1
  • 4Wahle A, Prause P M, Dejong S C, et al. Geometrically correct 3-D reconstruction of intravascular ultrasound images by fusion with bi- plane angiography methods and validation. IEEE Transactions on Medical Imaging, 1999 ; 18 (8) :686-699.
  • 5Lee S, Kim C, Oh D, et al. Three-dimensional intravascular optical coherence tomography rendering assessment of spontaneous coronary artery dissection concomitant with left main ostial critical stenosis. J Am Coll Cardiol Intv, 2014; 7 ( 6 ) : e57-e59, doi: 10. 1016/ j. jein. 2013.10. 023.
  • 6Chung W Y, Choi B J, Lim S H, et al. Three dimensional quantita- ti'qe coronary anglography can detect reliably ischemic coronary lesions based on fractional flow reserve. Journal of Korean Medical Science, 2015; 30(6) : 716-724.
  • 7Tu S, Xu L, Ligthart J, et aL In-vivo comparison of arterial lumen dimensions assessed by co-registered three-dimensional (3D) quanti- tative coronary angiography, intravascular ultrasound and optical co- herence tomography. Int J Cardiovasc Imaging, 2012 ; 28 : 1315-1327.
  • 8Janssen J P, Koning G, De Koning P J H, et al. A new approach tocontour detection in X-ray arteriograms : the wavecontour. Invest Ra- diol,2005 ; 40:514-520.
  • 9Balzaui D, Bose D, Brands D, et al. Parallel simulation of patient- specific atherosclerotic arteries for the enhancement of intravascular ultrasound diagnostics. Engineering Computations, 2012; 29 ( 8 ) : 888-906.
  • 10舍恩哈根P蓍.(美)著.刘希茜,刘健,陈芸译轻松掌握血管内超声.北京:人民军医出版社,2009:1-95.

二级参考文献29

  • 1Clarke LP, Velthuizen RP, Camacho MA, et al. MRI segmentation: methods and applications [ J ]. MRI Resonance Imaging, 1995, 13(3) : 343 - 368.
  • 2Antoine Maintz JB, Viergever MA. A survey of medical image registration [J]. Medical Image Analysis, 1998, 2(1) : 1 - 36.
  • 3Thirlon JP. Image matching as a diffusion process: an analogy with Maxwell's demons [J]. Medical Image Analysis, 1998, 2(3): 243 -60.
  • 4Xie Zhiyong, Lydia Ng, Gee J. Two algorithms for non-rigid image registration and their evaluation [ A ]. In: Sonka JMFM, eds. Medical Imaging: Image Processing[C]. San Diego: SPIE, 2003, 5032: 157- 164.
  • 5Wang He, Dong Lei, Daniel J, et al. Validation of an accelerated 'demons' algorithm for deformable image registration in radiation therapy [J]. Phys Med Biol, 2005, 50(12) : 2887 - 2905.
  • 6Thirion JP. Fast non-rlgid matching of 3D medical images [ R ]. Technical Report, INRIA 2547, 1995.
  • 7Cachier P, Pennec X, Ayache N. Fast non rigid matching by gradient descent: study and improvements of the “Demons” algorithm [R]. Technical Report, INRIA 3706, 1999.
  • 8Cuadra MB. Atlas-based segmentation and classification of magnetic resonance brain images [D]. Switzerland: Ecole Polytechnique Federale De Lausanne, 2003.
  • 9Cachier P, Bardinet E, Dormont D, et al. Iconic feature based nonrigid registration: the PASHA algorithm [J]. Computer Vision and Image Understanding, 2003, 89: 272- 298.
  • 10左明雪.人体解剖生理学[M].2版.北京:高等教育出版社,2009.

共引文献3

同被引文献9

引证文献1

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部