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Pharmacokinetic Perfusion Curves Estimation for Liver Tumor Diagnosis from DCE-MRI

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Book cover Image Analysis and Recognition (ICIAR 2008)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5112))

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Abstract

Dynamic-Contrast Enhanced MRI (DCE-MRI) is a method to analyze the perfusion dynamics in the tissues. The contrast agent concentration along the time, after the bolus injection, depends on the type of tissue observed, namely on its vascularization density and metabolic activity. The number of acquired volumes in this type of exam is usually very small, typically < 10, and the volumes are misaligned due to respiratory and cardiac activities.

In this paper an algorithm to automatically characterize the malignancy of the tumor is presented based on the perfusion curves on each voxel of the tumor, obtained from DCE-MRI. A non-rigid registration procedure based on Mutual Information (MI) criterion is used to align the small volumes representing the region of interest (ROI) containing the tumor along the time. A pharmacokinetic (PK) third order linear model is estimated from the observations and its parameters are used to classify the malignancy of tumor.

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References

  1. Tofts, P.S.e.a.: 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)

    Article  Google Scholar 

  2. Gal, Y., Mehnert, A., Bradley, A., McMahon, K., Crozier, S.: An evaluation of four parametric models of contrast enhancement for dynamic contrast magnetic resonance imaging of the breast. In: Proceedings of the 29th Annual International Conference of the IEEE EMBS (2007)

    Google Scholar 

  3. Port, R.E., Knopp, M.V., Brix, G.: Dynamic contrast-enhanced MRI using Gd-DTPA: interindividual variability of the arterial input function and consequences for the assessment of kinetics in tumors. Magn. Reson. Med. 45(6), 1030–1038 (2001)

    Article  Google Scholar 

  4. Collins, D.J., Padhani, A.R.: Dynamic magnetic resonance imaging of tumor perfusion. Approaches and biomedical challenges. IEEE Eng. Med. Biol. Mag. 23(5), 65–83 (2004)

    Article  Google Scholar 

  5. Srikanchana1, R., Thomasson, D., Choyke, P., Dwyer, A.: A comparison of pharmacokinetic models of dynamic contrast enhanced mri. In: Proceedings of the 17th IEEE Symposium on Computer-Based Medical Systems (CBMS 2004) (2004)

    Google Scholar 

  6. Pluim, J., Maintz, J., Viergever, M.: Mutual-information-based registration of medical images: a survey. IEEE Transactions on Medical Imaging 22(8), 986–1004 (2003)

    Article  Google Scholar 

  7. Shanks, J.L.: Recursion filters for digital processing. Geophysics 32, 33–51 (1967)

    Article  Google Scholar 

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Aurélio Campilho Mohamed Kamel

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© 2008 Springer-Verlag Berlin Heidelberg

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Caldeira, L.L., Sanches, J.M. (2008). Pharmacokinetic Perfusion Curves Estimation for Liver Tumor Diagnosis from DCE-MRI. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2008. Lecture Notes in Computer Science, vol 5112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69812-8_78

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  • DOI: https://doi.org/10.1007/978-3-540-69812-8_78

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69811-1

  • Online ISBN: 978-3-540-69812-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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