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Linearization improves the repeatability of quantitative dynamic contrast-enhanced MRI.

Kyle M Jones1, Mark D Pagel1, Julio Cárdenas-Rodríguez1

  • 1Department of Biomedical Engineering, University of Arizona, Tucson, AZ, United States; Department of Medical Imaging, University of Arizona, Tucson, AZ, United States.

Magnetic Resonance Imaging
|November 21, 2017
PubMed
Summary
This summary is machine-generated.

Linearization significantly improves dynamic contrast-enhanced MRI (DCE-MRI) model repeatability. Linear models (LTM, LRRM) offer superior precision over nonlinear models for DCE-MRI analysis, matching semi-quantitative metric performance.

Keywords:
Dynamic contrast-enhanced MRILinear modelsPharmacokineticsReference region modelRepeatabilityTofts model

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Area of Science:

  • Medical Imaging
  • Biophysics
  • Quantitative MRI

Background:

  • Dynamic contrast-enhanced MRI (DCE-MRI) is crucial for assessing tumor characteristics.
  • Quantitative DCE-MRI models, such as Tofts and Reference Region Models (RRM), provide vital pharmacokinetic parameters.
  • Assessing the repeatability of these models is essential for reliable clinical translation.

Purpose of the Study:

  • To compare the repeatability of linear and nonlinear Tofts and RRM for DCE-MRI.
  • To evaluate the performance of quantitative and semi-quantitative DCE-MRI metrics.

Main Methods:

  • Analysis of simulated and experimental DCE-MRI data from 12 rats with C6 glioma over three days.
  • Application of linear and nonlinear Tofts models (LTM, NTM) to estimate Ktrans.
  • Application of linear and nonlinear RRM (LRRM, NRRM) to estimate R Ktrans.
  • Assessment of repeatability using within-subject coefficient of variation (wSCV) and percent intra-subject variation (iSV) via Gage R&R analysis.

Main Results:

  • Linear Reference Region Model (LRRM) showed a two-fold lower iSV for R Ktrans compared to NRRM.
  • Linear Tofts Model (LTM) was at least 50% more repeatable than NTM.
  • Semi-quantitative metrics (iauc64, MER) demonstrated repeatability comparable to LTM and LRRM.
  • iSV for iauc64 and MER were significantly lower than for slope and TTP.

Conclusions:

  • Linearization enhances DCE-MRI repeatability by at least 30% in both simulations and experiments.
  • Linear quantitative DCE-MRI models achieve repeatability comparable to semi-quantitative metrics.
  • Linear models offer improved precision for DCE-MRI analysis.