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Joint arterial input function and tracer kinetic parameter estimation from undersampled dynamic contrast-enhanced MRI

Yi Guo1, Sajan Goud Lingala1, Yannick Bliesener1

  • 1Ming Hsieh Department of Electrical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA.

Magnetic Resonance in Medicine
|September 15, 2017
PubMed
Summary

This study presents a new model-based reconstruction for brain tumor MRI, enabling accurate kinetic parameter and arterial input function (AIF) estimation from undersampled data. High-fidelity results were achieved even with significant undersampling.

Keywords:
DCE-MRIcompressed sensingkinetic modelingmodel-based reconstruction

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Area of Science:

  • Medical Imaging
  • Radiology
  • Biophysics

Background:

  • Dynamic contrast-enhanced MRI (DCE-MRI) is crucial for brain tumor characterization.
  • Undersampling in DCE-MRI significantly compromises image quality and quantitative analysis.
  • Accurate estimation of arterial input function (AIF) and kinetic parameters is essential for reliable tumor assessment.

Purpose of the Study:

  • To develop and evaluate a model-based reconstruction framework for joint AIF and kinetic parameter estimation from undersampled brain tumor DCE-MRI data.
  • To enable flexible integration of various tracer-kinetic (TK) models and solvers within the reconstruction framework.
  • To assess the performance of the proposed method using digital reference objects and real patient data.

Main Methods:

  • A model-based reconstruction framework was developed, incorporating TK models as consistency constraints.
  • Joint estimation of the AIF was performed alongside kinetic parameter mapping.
  • The method was validated using a digital reference object (DRO) and retrospectively undersampled DCE-MRI datasets.
  • Prospective undersampled DCE-MRI data was also utilized for evaluation.

Main Results:

  • The framework successfully generated accurate kinetic parameter maps with low error, comparable to fully sampled data, even at 60-fold undersampling.
  • Patient-specific AIF estimation achieved a normalized root-mean-squared-error below 8% at up to 100-fold undersampling.
  • High-resolution, whole-brain kinetic parameter maps and patient-specific AIF were reconstructed from prospectively undersampled data.

Conclusions:

  • The proposed model-based DCE-MRI reconstruction framework facilitates the use of diverse TK solvers with a model consistency constraint.
  • It enables accurate joint estimation of patient-specific AIF and kinetic parameters.
  • High-fidelity reconstruction of TK maps and AIF is achievable at substantial k,t-space undersampling levels (up to 100-fold).