Development of 3D Mapping Algorithm with Non Linear Curve Fitting Method in Dynamic Contrast Enhanced MRI

  • Yoon Seong-Ik (Department of Biomedical Engineering, College of Medicine, The Catholic University of Korea) ;
  • Jahng Geon-Ho (Department of Radiology, University of California at San Francisco) ;
  • Khang Hyun-Soo (Department of Radiology, Seoul Health College) ;
  • Kim Young-Joo (Department of Radiology, College of Medicine, The Catholic University of Korea) ;
  • Choe Bo-Young (Department of Biomedical Engineering, College of Medicine, The Catholic University of Korea)
  • Published : 2005.12.01

Abstract

Purpose: To develop an advanced non-linear curve fitting (NLCF) algorithm for dynamic susceptibility contrast study of brain. Materials and Methods: The first pass effects give rise to spuriously high estimates of $K^{trans}$ in voxels with large vascular components. An explicit threshold value has been used to reject voxels. Results: By using this non-linear curve fitting algorithm, the blood perfusion and the volume estimation were accurately evaluated in T2*-weighted dynamic contrast enhanced (DCE)-MR images. From the recalculated each parameters, perfusion weighted image were outlined by using modified non-linear curve fitting algorithm. This results were improved estimation of T2*-weighted dynamic series. Conclusion: The present study demonstrated an improvement of an estimation of kinetic parameters from dynamic contrast-enhanced (DCE) T2*-weighted magnetic resonance imaging data, using contrast agents. The advanced kinetic models include the relation of volume transfer constant $K^{trans}\;(min^{-1})$ and the volume of extravascular extracellular space (EES) per unit volume of tissue $\nu_e$.

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