Advertisement

Detection of Substances in the Left Atrial Appendage by Spatiotemporal Motion Analysis Based on 4D-CT

  • Cheng Jin
  • Heng Yu
  • Jianjiang Feng
  • Lei Wang
  • Jiwen Lu
  • Jie Zhou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10663)

Abstract

The detection of substances in the left atrial appendage (LAA) is essential in evaluating disease development and treatment planning in patients with atrial fibrillation. The advent of 4D-CT bringing high spatiotemporal resolution, we present a new approach for the detection of substances in the LAA by spatiotemporal motion analysis and make a detailed judgment and analysis of spatial distribution and classification of most objects in the LAA. The noise interference is also eliminated properly. This approach requires the extraction of the optical flow field for all adjacent phases in a cardiac cycle of 20 phases. According to the optical flow information of 19 optical flow fields, we adopt the nearest neighbor interpolation method to establish the motion trajectory of the key voxels in a whole cardiac cycle. Then we create a hierarchical clustering tree by calculating the similarity between the tracks based on hierarchical clustering and find the corresponding classification for every track. Different classifications of tracks represent the divisions of substances in the LAA. Finally, we perform the stress and strain detection of the critical lump using time-frequency analysis of their trajectories. Tests and validations of our approach were performed on 32 data sets (artificial diagnosis of echocardiography and 4-D CT). The frequency responded range to stress and strain of different substances was obtained, which included normal circulation blood, mild, moderate and severe SEC blood, initial jelling thrombi, old or calcified thrombi, organic thrombi and pectinate muscles. Our results are consistent with the two artificial diagnoses. Furthermore, they can refine the identification of substances such as their texture and tiny sizes.

Keywords

Left atrial appendage (LAA) Thrombus 4D-CT Optical flow Hierarchical clustering Time-frequency analysis 

Notes

Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grants 61622207, 61373074, 61225008, and 61572271.

References

  1. 1.
    Holmes, D.R., Reddy, V.Y., Turi, Z.G., Doshi, S.K., Sievert, H., Buchbinder, M., Mullin, C.M., Sick, P.: Percutaneous closure of the left atrial appendage versus warfarin therapy for prevention of stroke in patients with atrial fibrillation: a randomised non-inferiority trial. Lancet 374(9689), 534–542 (2009)CrossRefGoogle Scholar
  2. 2.
    Calvert, P.A., Rana, B.S., Begley, D.A., Shapiro, L.M.: Occlusion of left atrial appendage to treat atrial fibrillation. Lancet 374(9703), 1742–1743 (2009)CrossRefGoogle Scholar
  3. 3.
    Zahnd, G., Salles, S., Liebgott, H., Vray, D., Serusclat, A., Moulin, P.: Real-time ultrasound-tagging to track the 2D motion of the common carotid artery wall in vivo. Med. Phys. 42(2), 820–830 (2015)CrossRefGoogle Scholar
  4. 4.
    Goncalves, I.B., Leiria, A., Moura, M.M.M.: STFT or CWT for the detection of Doppler ultrasound embolic signals. Int. J. Numer. Methods Biomed. Eng. 29(9), 964–976 (2013)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Wang, L., Feng, J., Jin, C., Lu, J., Zhou, J.: Left atrial appendage segmentation based on ranking 2-D segmentation proposals. In: Mansi, T., McLeod, K., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds.) STACOM 2016. LNCS, vol. 10124, pp. 21–29. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-52718-5_3 CrossRefGoogle Scholar
  6. 6.
    Baker, S., Scharstein, D., Lewis, J.P., Roth, S., Black, M.J., Szeliski, R.: A database and evaluation methodology for optical flow. Int. J. Comput. Vis. 92(1), 1–31 (2011)CrossRefGoogle Scholar
  7. 7.
    Lehmann, T.M., Gonner, C., Spitzer, K.: Survey: interpolation methods in medical image processing. IEEE Trans. Med. Imaging 18(11), 1049–1075 (1999)CrossRefGoogle Scholar
  8. 8.
    Liu, A.-A., Yu-Ting, S., Nie, W.-Z., Kankanhalli, M.: Hierarchical clustering multi-task learning for joint human action grouping and recognition. IEEE Trans. Patt. Anal. Mach. Intell. 39(1), 102–114 (2017)CrossRefGoogle Scholar
  9. 9.
    Gröchenig, K.: Foundations of Time-Frequency Analysis. Springer (2013)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Tsinghua National Laboratory for Information Science and Technology, Department of AutomationTsinghua UniversityBeijingChina

Personalised recommendations