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A New Transfer Function for Volume Visualization of Aortic Stent and Its Application to Virtual Endoscopy

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Published:21 June 2020Publication History
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Abstract

Aortic stent has been widely used in restoring vascular stenosis and assisting patients with cardiovascular disease. The effective visualization of aortic stent is considered to be critical to ensure the effectiveness and functions of the aortic stent in clinical practice. Volume rendering with ray casting has been used as an effective approach to enable the effective visualization of aortic stent. The volume rendering relies on the transfer function that converts the medical images into optical attributes including color and transparency. This article proposes a new transfer function, namely, the multi-dimensional transfer function, to provide additional transparency value of a voxel. The proposed approach using the additional transparency value effectively assists the distinguishing of tissues that have the same CT value. The transparency values are simultaneously determined by gray threshold and gray change threshold, which can recognize the unnecessary structures such as bones transparent. A series of experimental results demonstrate that the situation of aorta stent of a patient can be directly observed, and the angle of view can be switched arbitrarily. The proposed method provides a new way for the operation of a virtual endoscopy to reach the place of blood vessels that a traditional endoscopy fails to reach.

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References

  1. Chenxi Huang et al. 2018. A new framework for the integrative analytics of intravascular ultrasound and optical coherence tomography images. IEEE Access 6 (2018), 36408--36419.Google ScholarGoogle ScholarCross RefCross Ref
  2. D. S. Huang. 1996. Systematic theory of neural networks for pattern recognition (in Chinese). Pub. House Electron. Industr. China 201 (May 1996).Google ScholarGoogle Scholar
  3. D. S. Huang and Wen Jiang. 2012. A general CPL-AdS methodology for fixing dynamic parameters in dual environments. IEEE Trans. Syst. Man Cybern. Part B 42, 5 (2012), 1489--1500.Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Jie Peng et al. 2018. Diagnostic value of spectral CT for removal plaque sclerosis artifact of coronary artery calcification in cardiovascular stenosis. BME Clin. Med. 22, 2.Google ScholarGoogle Scholar
  5. G. Zhang, M. Pu, Y. Gu, and X. Zhou. 2019. Predicting aortic regurgitation after transcatheter aortic valve replacement by finite element method. IEEE Access 7 (2019), 64315--64322.Google ScholarGoogle ScholarCross RefCross Ref
  6. G. Zhang, L. You, L. Lan, N. Zeng, W. Chen, G. G. Poehling, and X. Zhou. 2019. Risk prediction model for knee arthroplasty. IEEE Access 7 (2019), 34645--34654.Google ScholarGoogle ScholarCross RefCross Ref
  7. Chenxi Huang et al. 2018. Automatic quantitative analysis of bioresorbable vascular scaffold struts in optical coherence tomography images using region growth. J. Med. Imag. Health Industr. 8, 1 (2018), 98--104.Google ScholarGoogle Scholar
  8. Chenxi Huang et al. 2018. Automatic side branch detection in optical coherence tomography images using adjacent frame correlation information. J. Med. Imag. Health Industr. 8, 7 (2018), 1513--1518.Google ScholarGoogle Scholar
  9. G. Zhang, J. J. Xia, M. Liebschner, X. Zhang, D. Kim, X. Zhou. 2016. Improved Rubin--Bodner model for the prediction of soft tissue deformations. Med. Eng. Phys. 38, 11 (2016), 1369--1375.Google ScholarGoogle ScholarCross RefCross Ref
  10. G. Zhang et al. 2016. A systematic approach to predicting the risk of unicompartmental knee arthroplasty revision. Osteoarth. Cartil. 24, 6 (2016), 991--999.Google ScholarGoogle ScholarCross RefCross Ref
  11. Hideyuki Kawashima, Yusuke Watanabe, and Ken Kozuma. 2017. Successful transfemoral aortic valve implantation through aortic stent graft after endovascular repair of abdominal aortic aneurysm. Cardiovasc. Interv. Therap. 32, 2 (2017), 165--169.Google ScholarGoogle ScholarCross RefCross Ref
  12. C. T. Mendonça et al. 2013. The use of a self-expandable aortic stent to incarcerate microcoils and to create a favourable infrarenal neck in an unusual case of endovascular abdominal aortic aneurysm repair. Euro. J. Vasc. Endovasc. Surg. 45, 5 (2013), 465--467.Google ScholarGoogle ScholarCross RefCross Ref
  13. Shu-Lin Wang, Yihai Zhu, Wei Jia, and D. S. Huang. 2012. Robust classification method of tumor subtype by using correlation filters. IEEE/ACM Trans. Comput. Biol. Bioinf. 9, 2 (2012), 580--591.Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Chun-Hou Zheng, Lei Zhang, Vincent To-Yee Ng, Simon Chi-Keung Shiu, and D. S. Huang. 2011. Metasample-based sparse representation for tumor classification. IEEE/ACM Trans. Comput. Biol. Bioinf. 8, 5 (2011), 1273--1282.Google ScholarGoogle ScholarCross RefCross Ref
  15. Kiyoung Choi, Sungup Jo, Hwamin Lee, and Changsung Jeong. 2013. CPU-based speed acceleration techniques for shear warp volume rendering. Multimedia Tools Applic. 64, 2 (2013), 309--329.Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. G. Zhang, H. Tan, X. Qian, J. Zhang, K. Li, L. R. David, and X. Zhou. 2016. A systematic approach to predicting spring force for sagittal craniosynostosis surgery. J. Craniofac. Surg. 27, 3 (2016), 636--643.Google ScholarGoogle ScholarCross RefCross Ref
  17. Chenxi Huang et al. 2018. A novel WebVR-based lightweight framework for virtual visualization of blood vasculum. IEEE Access 6 (2018), 27726--27735.Google ScholarGoogle ScholarCross RefCross Ref
  18. Reza Hashemian, Mounika Vanga, and Mahmoud Rahat. 2017. Identifying and 3D displaying poles and zeros in analog circuit transfer functions: Bode surfaces. Circ. Syst. Sig. Pr. 36, 6 (2017), 2473--2485.Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. M. S. Salim, Naseer Sabri, and S. Fouad. 2017. Modelling of acoustic impedance transfer function for liquids subjected to a centrifugation process. Arab J. Sci. Eng. 42, 7 (2017), 2717--2726.Google ScholarGoogle ScholarCross RefCross Ref
  20. Yong Gwon Kim and Yeunchul Ryu. 2017. Accurate evaluation of modulation transfer function using the Fourier shift theorem. J. Korean Phys. Soc. 71, 12 (2017), 1064--1068.Google ScholarGoogle ScholarCross RefCross Ref
  21. Xiao-Feng Wang, D. S. Huang, and Huan Xu. 2010. An efficient local Chan-Vese model for image segmentation. Pattern Recog. 43, 3 (2010), 603--618.Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Chun-Hou Zheng, D. S. Huang, Lei Zhang, and Xiang-Zhen Kong. 2009. Tumor clustering using non-negative matrix factorization with gene selection. IEEE Trans. Inf. Technol. Biomed. 13, 4 (2009), 599--607.Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Taosong He et al. 1996. Generation of transfer functions with stochastic search techniques. In Proceedings of the 7th Conference on Visualization. 227--234.Google ScholarGoogle Scholar
  24. Baoguang Liu, Lida Zhu, Yichao Dun, and Changfu Liu. 2017. Investigation on chatter stability of thin-walled parts in milling based on process damping with relative transfer functions. Int. J. Adv. Manuf. Technol. 89 (2017), 2701--2711.Google ScholarGoogle ScholarCross RefCross Ref
  25. Pachaya Sailamul, Jaeson Jang, and Se-Bum Paik. 2017. Synaptic convergence regulates synchronization-dependent spike transfer in feedforward neural networks. J. Comput. Neurosci. 43, 3 (2017), 189--202.Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Norikazu Suzuki, Yusuke Kurata, Takashi Kato, Rei Hino, and Eiji Shamoto. 2012. Identification of transfer function by inverse analysis of self-excited chatter vibration in milling operations. Precis. Eng. 36, 4 (2012), 568--575.Google ScholarGoogle ScholarCross RefCross Ref
  27. D. S. Huang and Chun-Hou Zheng. 2006. Independent component analysis based penalized discriminant method for tumor classification using gene expression data. Bioinformatics 22, 15 (2006), 1855--1862.Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Chenxi Huang et al. 2018. A hybrid active contour segmentation method for myocardial D-SPECT images. IEEE Access 6, (2018), 39334--39343.Google ScholarGoogle Scholar
  29. Chenxi Huang et al. 2017. Fusion of optical coherence tomography and angiography for numerical simulation of hemodynamics in bioresorbable stented coronary artery based on patient-specific model. Comput. Assist. Surg. 22 (2017), 127--134.Google ScholarGoogle ScholarCross RefCross Ref
  30. D. S. Huang, Ji-Xiang Du. 2008. A constructive hybrid structure optimization methodology for radial basis probabilistic neural networks. IEEE Trans. Neur. Netw. 19, 12 (2008), 2099--2115.Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. D. S. Huang. 1999. Radial basis probabilistic neural networks: Model and application. Int. J. Pattern Recog. Artif. Intell. 13, 7 (1999), 1083--1101.Google ScholarGoogle ScholarCross RefCross Ref
  32. M. Levoy. 1988. Display of surfaces from volume data. IEEE Comput. Graph. 8, 3 (1988), 29--37.Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. D. J. Vining, D. W. Gelfand, and R. E. Bechtold. 1994. Technical feasibility of colon imaging with helical CT and virtual reality. In Proceedings of the Annual Meeting of American Roentgen Society.Google ScholarGoogle Scholar
  34. I. Bitter, A. Kaufman, and M. Sato. 2001. Penalized-distance volumetric skeleton algorithm. IEEE Trans. Vis. Comput. Graph 3 (2001), 195--203.Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. T. Nakasato et al. 2001. Virtual CT endoscopy of ossicles in the middle ear. Clin. Imag. 25, 3 (2001), 171--177.Google ScholarGoogle ScholarCross RefCross Ref
  36. Ping Han et al. 2000. Virtual endoscopy of the nasal cavity in comparison with fiberoptic endoscopy. Eur. Arch. Oto.-Rhino.-L. 257, 10 (2000), 578--583.Google ScholarGoogle ScholarCross RefCross Ref
  37. Kazutaka Yamada, Manabu Morimoto, Miori Kishimoto, and Erik R. Wisner. 2007. Virtual endoscopy of dogs using multi‐detector row CT. Vet. Radiol. Ultras. 48, 4 (2007), 318--322.Google ScholarGoogle ScholarCross RefCross Ref
  38. Xiao-Feng Wang, D. S. Huang, 2009. A novel density-based clustering framework by using level set method. IEEE Trans. Knowl. Data Eng. 21, 11 (2009), 1515--1531.Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Bjoern G. Volkmer et al. 2002. Evaluation of disintegration in prevesical ureteral calculi by 3-dimensional endo-ultrasound with surface rendering. J. Urol. 168, 2 (2002), 450--453.Google ScholarGoogle ScholarCross RefCross Ref
  40. T. Xi et al. 2013. Validation of a novel semi-automated method for three-dimensional surface rendering of condyles using cone beam computed tomography data. Int. J. Oral Maxill. Surg. 42, 8 (2013), 1023--1029.Google ScholarGoogle ScholarCross RefCross Ref
  41. Joseph Holub and Eliot Winer. 2017. Enabling real-time volume rendering of functional magnetic resonance imaging on an iOS Device. J. Dig. Imag. 30, 6 (2017), 738--750.Google ScholarGoogle ScholarCross RefCross Ref
  42. Patric Ljung et al. 2016. State of the art in transfer functions for direct volume rendering. Comput. Graph. Forum 35, 3 (2016), 669--691.Google ScholarGoogle ScholarCross RefCross Ref
  43. Joe Kniss, Gordon Kindlmann, and Charles Hansen. 2002. Multidimensional transfer functions for interactive volume rendering. IEEE Trans. Vis. Comput. Graph. 8, 3 (2002), 270--285.Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. Yiipeng Song, Jie Yang, Lei Zhou, and Yuemin Zhu. 2015. Electric-field-based transfer functions for volume visualization. J. Med. Biol. Eng. 35 (2015), 270--277.Google ScholarGoogle ScholarCross RefCross Ref
  45. Xiao-Feng Wang, D. S. Huang. 2009. A novel density-based clustering framework by using level set method. IEEE Trans. Knowl. Data Eng. 21, 11 (2009), 1515--1531.Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. Michael Teistler, Richard S. Breiman, Sauw Ming Liong, Liang Yoong Ho, Arif Shahab, and Wieslaw L. Nowinski. 2007. Interactive definition of transfer functions in volume rendering based on image markers. Int. J. Cars. 2 (2007), 55--64.Google ScholarGoogle ScholarCross RefCross Ref
  47. C. Guiver, H. Logemann, and M. R. Opmeer. 2017. Transfer functions of infinite-dimensional systems: Positive realness and stabilization. Math. Contr. Sig. Syst. 20 (2017).Google ScholarGoogle Scholar
  48. Nelson Max. 1995. Optical models for direct volume rendering. IEEE Trans. Vis. Comput. Graph. 1, 2 (1995), 99--108.Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. Yuzhi Kang. 2009. GPU Programming and Cg Language Primer 1rd Edition. Banshan studio.Google ScholarGoogle Scholar
  50. Mathias Sawall and Klaus Neymeyr. 2016. A ray casting method for the computation of the area of feasible solutions for multicomponent systems: Theory, applications and FACPACK—implementation. Analyt. Chim. Acta, 2016. DOI:10.1016/j.aca.2016.11.069Google ScholarGoogle Scholar
  51. Yosuke Higo et al. 2014. Trinarization of μX-ray CT images of partially saturated sand at different water-retention states using a region growth method. Nucl. Inst. Meth. Phys. Res. 324 (2014), 63--69.Google ScholarGoogle ScholarCross RefCross Ref
  52. Fei Yang, Fan Zhou, Ruo-mei Wang, Li Liu, and Xiao-nan Luo. 2014. A fast and efficient mesh segmentation method based on improved region growth. Appl. Math. Series B 29, 4 (2014), 468--480.Google ScholarGoogle ScholarCross RefCross Ref
  53. Weibo Xu, Ying Liu, and Haowei Zhang. 2017. Research progress in image segmentation based on region growing. Beijing Med. Eng. 36, 3 (2017).Google ScholarGoogle Scholar

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          cover image ACM Transactions on Multimedia Computing, Communications, and Applications
          ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 16, Issue 2s
          Special Issue on Smart Communications and Networking for Future Video Surveillance and Special Section on Extended MMSYS-NOSSDAV 2019 Best Papers
          April 2020
          291 pages
          ISSN:1551-6857
          EISSN:1551-6865
          DOI:10.1145/3407689
          Issue’s Table of Contents

          Copyright © 2020 ACM

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          Publication History

          • Published: 21 June 2020
          • Online AM: 7 May 2020
          • Revised: 1 November 2019
          • Accepted: 1 November 2019
          • Received: 1 August 2019
          Published in tomm Volume 16, Issue 2s

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