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Dynamic contrast enhanced (DCE) MRI estimation of vascular parameters using knowledge-based adaptive models.

Hassan Bagher-Ebadian1,2,3,4, Stephen L Brown5,6,7, Mohammad M Ghassemi8

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Four adaptive models (AMs) estimate microvascular parameters from Dynamic Contrast-Enhanced MRI without an Arterial-Input Function (AIF). This method provides stable, improved quantification of tumor and normal tissue microvasculature compared to conventional approaches.

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Area of Science:

  • Medical Imaging
  • Biophysics
  • Pharmacokinetics

Background:

  • Dynamic Contrast-Enhanced (DCE) MRI is crucial for assessing microvascular parameters.
  • Conventional DCE-MRI analysis often relies on an Arterial-Input Function (AIF), which can be challenging to obtain accurately.
  • Accurate estimation of microvascular parameters like Ktrans, vp, and ve is vital for understanding tissue physiology and disease.

Purpose of the Study:

  • To introduce and validate four adaptive models (AMs) for estimating microvascular parameters from DCE-MRI data.
  • To perform physiologically based Nested-Model-Selection (NMS) estimation without requiring an AIF.
  • To improve the stability and accuracy of DCE-MRI based quantification of microvasculature.

Main Methods:

  • Development and validation of four AMs using a nested-cross-validation (NCV) approach.
  • Extraction of 190 features from raw DCE-MRI data to construct the AMs.
  • Application of an extended Patlak-based NMS paradigm and NMS-based a priori knowledge for parameter estimation in 66 rats.

Main Results:

  • AMs produced stable maps of vascular parameters and nested-model regions, less affected by AIF-dispersion compared to conventional analysis.
  • High performance metrics were achieved: 0.914/0.834 for regions, 0.825/0.720 for vp, 0.938/0.880 for Ktrans, and 0.890/0.792 for ve (Correlation coefficient/Adjusted R-squared).
  • The AMs demonstrated improved quantification of microvasculature properties.

Conclusions:

  • Adaptive models offer a robust method for DCE-MRI based microvascular quantification without an AIF.
  • This approach quickens and enhances the accuracy of DCE-MRI analysis for both tumors and normal tissues.
  • The validated AMs represent a significant advancement over conventional DCE-MRI analysis techniques.