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An in silico validation framework for quantitative DCE-MRI techniques based on a dynamic digital phantom.

Chengyue Wu1, David A Hormuth2, Ty Easley3

  • 1Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, 201 E 24th St, Austin, TX 78712, United States.

Medical Image Analysis
|July 30, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel digital phantom and simulation framework for validating dynamic contrast-enhanced MRI (DCE-MRI) methods. The framework accurately models image acquisition parameters, demonstrating their impact on image quality and pharmacokinetic measurements.

Keywords:
Computational fluid dynamicsHemodynamicsKidneyMRI simulatorPharmacokineticsQuantitative MRI

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Area of Science:

  • Medical Imaging
  • Biophysics
  • Computational Fluid Dynamics

Background:

  • Quantitative evaluation of image processing methods is crucial for utility and guiding data acquisition.
  • Obtaining "ground truth" data experimentally for validation is challenging.
  • Digital phantoms offer a solution by providing numerical models with known biophysical properties.

Purpose of the Study:

  • To propose an in silico validation framework for dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) acquisition and analysis.
  • To develop a novel dynamic digital phantom simulating blood-interstitial flow and contrast agent delivery.
  • To establish a virtual simulator generating realistic DCE-MRI data with controllable parameters.

Main Methods:

  • A 1D-3D coupled computational fluid dynamic system was used to model blood-interstitial flow.
  • An advection-diffusion equation described contrast agent delivery within the digital phantom.
  • A virtual simulator generated DCE-MRI data based on the digital phantom and controllable acquisition parameters.

Main Results:

  • Contrast-to-noise ratio (CNR) significantly decreased with lower spatial resolution (SR) or signal-to-noise ratio (SNR) in standard acquisition (Protocol A).
  • Error in signal-enhancement ratio (PE_SER) decreased with increased temporal resolution (TR) in ultra-fast acquisition (Protocol B).
  • Acquisition parameters (SNR, SR, TR) were shown to significantly impact the ability of DCE-MRI to capture morphological and pharmacokinetic features.

Conclusions:

  • The proposed in silico framework effectively generates virtual MR images reflecting the impact of acquisition parameters.
  • This validation framework is valuable for investigating perfusion-based MRI techniques.
  • The framework facilitates systematic evaluation and optimization of novel MRI acquisition, reconstruction, and image processing techniques.