Efficient 4D Non-local Tensor Total-Variation for Low-Dose CT Perfusion Deconvolution

  • Ruogu FangEmail author
  • Ming Ni
  • Junzhou Huang
  • Qianmu Li
  • Tao Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9601)


Tensor total variation deconvolution has been recently proposed as a robust framework to accurately estimate the hemodynamic parameters in low-dose CT perfusion by fusing the local anatomical structure correlation and the temporal blood flow continuation. However the locality property in the current framework constrains the search for anatomical structure similarities to the local neighborhood, missing the global and long-range correlations in the whole anatomical structure. This limitation has led to the noticeable absence or artifacts of delicate structures, including the critical indicators for the clinical diagnosis of cerebrovascular diseases. In this paper, we propose an extension of the TTV framework by introducing 4D non-local tensor total variation into the deconvolution to bridge the gap between non-adjacent regions of the same tissue classes. The non-local regularization using tensor total variation term is imposed on the spatio-temporal flow-scaled residue functions. An efficient algorithm and the implementation of the non-local tensor total variation (NL-TTV) reduce the time complexity with the fast similarity computation, the accelerated optimization and parallel operations. Extensive evaluations on the clinical data with cerebrovascular diseases and normal subjects demonstrate the importance of non-local linkage and long-range connections for the low-dose CT perfusion deconvolution.


Compute Tomography Perfusion Arterial Input Function Search Window Tissue Class Finite Difference Operator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Saito, N., Kudo, K., Sasaki, T., Uesugi, M., Koshino, K., Miyamoto, M., Suzuki, S.: Realization of reliable cerebral-blood-flow maps from low-dose CT perfusion images by statistical noise reduction using nonlinear diffusion filtering. Radiol. Phys. Technol. 1(1), 62–74 (2008)CrossRefGoogle Scholar
  2. 2.
    Mendrik, A.M., Vonken, E., van Ginneken, B., de Jong, H.W., Riordan, A., van Seeters, T., Smit, E.J., Viergever, M.A., Prokop, M.: Tips bilateral noise reduction in 4d CT perfusion scans produceshigh-quality cerebral blood flow maps. Phys. Med. Biol. 56(13), 3857 (2011)CrossRefGoogle Scholar
  3. 3.
    Tian, Z., Jia, X., Yuan, K., Pan, T., Jiang, S.B.: Low-dose CT reconstruction via edge-preserving total variation regularization. Phys. Med. Biol. 56(18), 5949 (2011)CrossRefGoogle Scholar
  4. 4.
    Ma, J., Huang, J., Feng, Q., Zhang, H., Lu, H., Liang, Z., Chen, W.: Low-dose computed tomography image restoration using previous normal-dose scan. Med. Phys. 38, 5713 (2011)CrossRefGoogle Scholar
  5. 5.
    Supanich, M., Tao, Y., Nett, B., Pulfer, K., Hsieh, J., Turski, P., Mistretta, C., Rowley, H., Chen, G.H.: Radiation dose reduction in time-resolved CT angiography using highly constrained back projection reconstruction. Phys. Med. Biol. 54(14), 4575 (2009)CrossRefGoogle Scholar
  6. 6.
    He, L., Orten, B., Do, S., Karl, W.C., Kambadakone, A., Sahani, D.V., Pien, H.: A spatio-temporal deconvolution method to improve perfusion CT quantification. IEEE Trans. Med. Imaging 29(5), 1182–1191 (2010)CrossRefGoogle Scholar
  7. 7.
    Fang, R., Chen, T., Sanelli, P.C.: Towards robust deconvolution of low-dose perfusion CT: sparse perfusion deconvolution using online dictionary learning. Med. Image Anal. 17(4), 417–428 (2013)CrossRefGoogle Scholar
  8. 8.
    Fang, R., Karlsson, K., Chen, T., Sanelli, P.C.: Improving low-dose blood-brain barrier permeability quantification using sparse high-dose induced prior for patlak model. Med. Image Anal. 18(6), 866–880 (2014)CrossRefGoogle Scholar
  9. 9.
    Fang, R., Zhang, S., Chen, T., Sanelli, P.: Robust low-dose CT perfusion deconvolution via tensor total-variation regularization. IEEE Trans. Med. Imaging 34(7), 1533–1548 (2015)CrossRefGoogle Scholar
  10. 10.
    Yu, Y., Zhang, S., Li, K., Metaxas, D., Axel, L.: Deformable models with sparsity constraints for cardiac motion analysis. Med. Image Anal. 18(6), 927–937 (2014)CrossRefGoogle Scholar
  11. 11.
    Zhang, S., Zhan, Y., Dewan, M., Huang, J., Metaxas, D.N., Zhou, X.S.: Towards robust and effective shape modeling: sparse shape composition. Med. Image Anal. 16(1), 265–277 (2012)CrossRefGoogle Scholar
  12. 12.
    Fang, R., Sanelli, P.C., Zhang, S., Chen, T.: Tensor total-variation regularized deconvolution for efficient low-dose CT perfusion. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014, Part I. LNCS, vol. 8673, pp. 154–161. Springer, Heidelberg (2014)Google Scholar
  13. 13.
    Sawatzky, A.: (Nonlocal) Total Variation in Medical Imaging, Ph.D. ThesisGoogle Scholar
  14. 14.
    Zhang, X., Burger, M., Bresson, X., Osher, S.: Bregmanized nonlocal regularization for deconvolution and sparse reconstruction. SIAM J. Imaging Sci. 3(3), 253–276 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Mignotte, M.: A non-local regularization strategy for image deconvolution. Pattern Recogn. Lett. 29(16), 2206–2212 (2008)CrossRefGoogle Scholar
  16. 16.
    Elmoataz, A., Lezoray, O., Bougleux, S.: Nonlocal discrete regularization on weighted graphs: a framework for image and manifold processing. IEEE Trans. Image Process. 17(7), 1047–1060 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Jia, X., Lou, Y., Dong, B., Tian, Z., Jiang, S.: 4D computed tomography reconstruction from few-projection data via temporal non-local regularization. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part I. LNCS, vol. 6361, pp. 143–150. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  18. 18.
    Huang, J., Yang, F.: Compressed magnetic resonance imaging based on wavelet sparsity and nonlocal total variation. In: 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), pp. 968–971. IEEE (2012)Google Scholar
  19. 19.
    Britten, A., Crotty, M., Kiremidjian, H., Grundy, A., Adam, E.: The addition of computer simulated noise to investigate radiation dose and image quality in images with spatial correlation of statistical noise: an example application to X-ray CT of the brain. Br. J. Radiol. 77, 323–328 (2014)CrossRefGoogle Scholar
  20. 20.
    Juluru, K., Shih, J., Raj, A., Comunale, J., Delaney, H., Greenberg, E., Hermann, C., Liu, Y., Hoelscher, A., Al-Khori, N., et al.: Effects of increased image noise on image quality and quantitative interpretation in brain CT perfusion. Am. J. Neuroradiol. 34(8), 1506–1512 (2013)CrossRefGoogle Scholar
  21. 21.
    Frush, D.P., Slack, C.C., Hollingsworth, C.L., Bisset, G.S., Donnelly, L.F., Hsieh, J., Lavin-Wensell, T., Mayo, J.R.: Computer-simulated radiation dose reduction for abdominal multidetector CT of pediatric patients. Am. J. Roentgenol. 179(5), 1107–1113 (2002)CrossRefGoogle Scholar
  22. 22.
    Østergaard, L., Weisskoff, R.M., Chesler, D.A., Gyldensted, C., Rosen, B.R.: High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part I: Mathematical approach and statistical analysis. Magn. Reson. Med. 36(5), 715–725 (1996)CrossRefGoogle Scholar
  23. 23.
    Wittsack, H.J., Wohlschläger, A.M., Ritzl, E., Kleiser, R., Cohnen, M., Seitz, R., Mödder, U.: CT-perfusion imaging of the human brain: advanced deconvolution analysis using circulant singular value decomposition. Comput. Med. Imaging Graph. 32(1), 67–77 (2008)CrossRefGoogle Scholar
  24. 24.
    Fieselmann, A., Kowarschik, M., Ganguly, A., Hornegger, J., Fahrig, R.: Deconvolution-based CT and MR brain perfusion measurement: theoretical model revisited and practical implementation details. J. Biomed. Imaging 2011, 14 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Ruogu Fang
    • 1
    Email author
  • Ming Ni
    • 2
  • Junzhou Huang
    • 3
  • Qianmu Li
    • 2
  • Tao Li
    • 1
  1. 1.School of Computing and Information SciencesFlorida International UniversityMiamiUSA
  2. 2.School of Computer Science and EngineeringNanjing University of Science and TechnologyNanjingChina
  3. 3.Department of Computer Science and EngineeringUniversity of Texas at ArlingtonArlingtonUSA

Personalised recommendations