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Continuous-Time Flow-Limited Modeling by Convolution Area Property and Differentiation Product Rule in 4-Phase Liver Dynamic Contrast-Enhanced CT

  • Sang Ho Lee
  • Yasuji Ryu
  • Koichi Hayano
  • Hiroyuki Yoshida
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8198)

Abstract

Parametric-fitting approaches for tracer kinetic modeling depend on the capability of a computational method to describe underlying physiologic processes that cause temporal intensity changes in dynamic contrast-enhanced (DCE) images. Rapid scan techniques allow perfusion CT imaging with high temporal resolution. In clinical practice, however, the perfusion CT protocol is especially a trade-off between the number of data points and the total radiation dose. Considering availability and radiation exposure, use of DCE-CT imaging derived from 4 temporal phases, which include precontrast, arterial, portal, and delayed phases, is highly desirable for the liver. However, low-temporal- resolution images like 4-phase liver DCE-CT present several barriers to modeling of tracer kinetics because of the lack of temporal enhancement data, which limits obtaining reliable physiologic information. The major reason for the limited application of a tracer kinetic model in temporally sparse dynamic data is that general computational algorithms such as deconvolution techniques require discretizing of arterial (or portal-vein) and tissue curves for estimation of kinetic parameters, leading to an unstable computational solution. The numerical instability due to the discretization of the enhancement curves can be more pronounced in the low-temporal-resolution data like those gleaned from 4-phase DCE-CT. For this reason, we propose a novel dual-input continuous-time tracer kinetic modeling method based on a new mathematical approach that uses the convolution area property and the differentiation product rule, without any discretization of the enhancement curves. This model was applied to case studies of hepatocellular carcinoma in 4-phase DCE-CT to illustrate the potential effectiveness of continuous-time tracer kinetic modeling. The proposed analytic scheme was shown to be feasible for estimation of kinetic parameters even in 4-phase liver DCE-CT, potentially being a practical guide for tracer kinetic model-based curve-fitting in temporally sparse data.

Keywords

Continuous-time tracer kinetic modeling convolution area property differentiation product rule four-phase dynamic contrast-enhanced CT 

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References

  1. 1.
    Lee, T.Y., Purdie, T.G., Stewart, E.: CT imaging of angiogenesis. Q. J. Nucl. Med. 47(3), 171–187 (2003)Google Scholar
  2. 2.
    Lee, S.H., Cai, W., Yoshida, H.: Tracer kinetic modeling by morales-smith hypothesis in hepatic perfusion CT. In: Yoshida, H., Hawkes, D., Vannier, M.W. (eds.) Abdominal Imaging 2012. LNCS, vol. 7601, pp. 292–302. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  3. 3.
    Brix, G., Griebel, J., Kiessling, F., Wenz, F.: Tracer kinetic modelling of tumour angiogenesis based on dynamic contrast-enhanced CT and MRI measurements. Eur. J. Nucl. Med. Mol. Imaging 37(suppl. 1), S30–S51 (2010)Google Scholar
  4. 4.
    Konstas, A.A., Goldmakher, G.V., Lee, T.Y., Lev, M.H.: Theoretic basis and technical implementations of CT perfusion in acute ischemic stroke, part 1: Theoretic basis. AJNR Am. J. Neuroradiol. 30(4), 662–668 (2009)CrossRefGoogle Scholar
  5. 5.
    Koh, T.S., Cheong, D.L., Hou, Z.: Issues of discontinuity in the impulse residue function for deconvolution analysis of dynamic contrast-enhanced MRI data. Magn. Reson. Med. 66(3), 886–892 (2011)CrossRefGoogle Scholar
  6. 6.
    Lee, S.H., Kim, J.H., Kim, K.G., Park, S.J., Im, J.G.: Application of time sampling in brain CT perfusion imaging for dose reduction. In: Proc. SPIE Medical Imaging 2007: Physics of Medical Imaging, vol. 6510, pp. 65102P-1–65102P-10 (2007)Google Scholar
  7. 7.
    Bisdas, S., Foo, C.Z., Thng, C.H., Vogl, T.J., Koh, T.S.: Optimization of perfusion CT protocol for imaging of extracranial head and neck tumors. J. Digit. Imaging 22(5), 437–448 (2009)CrossRefGoogle Scholar
  8. 8.
    Pandharipande, P.V., Krinsky, G.A., Rusinek, H., Lee, V.S.: Perfusion imaging of the liver: Current challenges and future goals. Radiology 234(3), 661–673 (2005)CrossRefGoogle Scholar
  9. 9.
    Kambadakone, A.R., Sharma, A., Catalano, O.A., Hahn, P.F., Sahani, D.V.: Protocol modifications for CT perfusion (CTp) examinations of abdomen-pelvic tumors: Impact on radiation dose and data processing time. Eur. Radiol. 21(6), 1293–1300 (2011)CrossRefGoogle Scholar
  10. 10.
    Catalano, O., Cusati, B., Sandomenico, F., Nunziata, A., Lobianco, R., Siani, A.: Multiple-phase spiral computerized tomography of small hepatocellular carcinoma: Technique optimization and diagnostic yield. Radiol. Med. 98(1-2), 53–64 (1999)Google Scholar
  11. 11.
    Kim, S.K., Lim, J.H., Lee, W.J., Kim, S.H., Choi, D., Lee, S.J., Lim, H.K., Kim, H.: Detection of hepatocellular carcinoma: Comparison of dynamic three-phase computed tomography images and four-phase computed tomography images using multidetector row helical computed tomography. J. Comput. Assist. Tomogr. 26(5), 691–698 (2002)CrossRefGoogle Scholar
  12. 12.
    Orton, M.R., d’Arcy, J.A., Walker-Samuel, S., Hawkes, D.J., Atkinson, D., Collins, D.J., Leach, M.O.: Computationally efficient vascular input function models for quantitative kinetic modelling using DCE-MRI. Phys. Med. Biol. 53(5), 1225–1239 (2008)CrossRefGoogle Scholar
  13. 13.
    Bae, K., Heiken, J., Brink, J.: Aortic and hepatic contrast medium enhancement at CT. I. Prediction with a computer model. Radiology 207, 647–655 (1998)Google Scholar
  14. 14.
    Tofts, P.S., Brix, G., Buckley, D.L., Evelhoch, J.L., Henderson, E., Knopp, M.V., Larsson, H.B., Lee, T.Y., Mayr, N.A., Parker, G.J., Port, R.E., Taylor, J., Weisskoff, R.M.: Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: Standardized quantities and symbols. J. Magn. Reson Imaging 10(3), 223–232 (1999)CrossRefGoogle Scholar
  15. 15.
    Riad, S.M.: The deconvolution problem: An overview. Proc. IEEE 74(1), 82–85 (1986) Google Scholar
  16. 16.
    Zhu, F., Carpenter, T., Rodriguez Gonzalez, D., Atkinson, M., Wardlaw, J.: Computed tomography perfusion imaging denoising using gaussian process regression. Phys. Med. Biol.  57(12), N183–N198 (2012)Google Scholar
  17. 17.
    Ibanez, L., Schroeder, W., Ng, L., Cates, J.: The ITK software guide. Kitware, Inc., Clifton Park (2005)Google Scholar
  18. 18.
    Miles, K.A., Hayball, M.P., Dixon, A.K.: Functional images of hepatic perfusion obtained with dynamic CT. Radiology 188(2), 405–411 (1993)Google Scholar
  19. 19.
    Tsushima, Y., Funabasama, S., Aoki, J., Sanada, S., Endo, K.: Quantitative perfusion map of malignant liver tumors, created from dynamic computed tomography data. Acad. Radiol. 11(2), 215–223 (2004)CrossRefGoogle Scholar
  20. 20.
    Matsui, O., Kadoya, M., Kameyama, T., Yoshikawa, J., Takashima, T., Nakanuma, Y., Unoura, M., Kobayashi, K., Izumi, R., Ida, M.: Benign and malignant nodules in cirrhotic livers: Distinction based on blood supply. Radiology 178(2), 493–497 (1991)Google Scholar
  21. 21.
    Chiandussi, L., Greco, F., Sardi, G., Vaccarino, A., Ferraris, C.M., Curti, B.: Estimation of hepatic arterial and portal venous blood flow by direct catheterization of the vena porta through the umbilical cord in man. Preliminary results. Acta Hepatosplenol. 15(3), 166–171 (1968)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Sang Ho Lee
    • 1
  • Yasuji Ryu
    • 1
  • Koichi Hayano
    • 2
  • Hiroyuki Yoshida
    • 1
  1. 1.3D Imaging Research, Department of RadiologyMassachusetts General Hospital and Harvard Medical SchoolBostonUSA
  2. 2.Division of Abdominal Imaging and Intervention, Department of RadiologyMassachusetts General HospitalBostonUSA

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