The goal of this study was to optimize dynamic contrast-enhanced (DCE)-MRI analysis for differently sized contrast agents and to evaluate the sensitivity for microvascular differences in skeletal muscle.Methods:
In rabbits, pathophysiological perfusion differences between hind limbs were induced by unilateral femoral artery ligation. On days 14 and 21, DCE-MRI was performed using a medium-sized contrast agent (MCA) (Gadomer) or a small contrast agent (SCA) (Gd-DTPA). Acquisition protocols were adapted to the pharmacokinetic properties of the contrast agent. Model-based data analysis was optimized by selecting the optimal model, considering fit error, estimation uncertainty, and parameter interdependency from three two-compartment pharmacokinetic models (normal and extended generalized kinetic models and Patlak model). Model-based parameters were compared to the model-free parameter area-under-curve (AUC). Finally, the sensitivity of transfer constant and AUC for physiological and pathophysiological microvascular differences was evaluated.Results:
For the MCA, the optimal model included and plasma fraction . For the SCA, and interstitial fraction should be incorporated. For the MCA, were ( error) and for the red soleus and white tibialis muscle, respectively, . With the SCA, were (soleus) and (tibialis) . In the ischemic limb, was significantly decreased relative to the control limb (soleus: 15%–20%; tibialis: 5%–10%). Similar differences in AUC were found for both contrast agents.Conclusions:
For optimal estimation of microvascular parameters, both model-based and model-free analysis should be adapted to the pharmacokinetic properties of the contrast agent. The detection of microvascular differences based on both and AUC was most sensitive when the analysis strategy was tailored to the contrast agent used. The MCA was equally sensitive for microvascular differences as the SCA, with the advantage of improved spatial resolution.
The authors are grateful to Viviane Heijnen for her support during the experiments and to Etienne Lemaire and Eveline Peeters for their enormous help with the data acquisition. They thank Bayer Schering Pharma AG for providing Gadomer for this study. This study was supported by The Netherlands Heart Foundation (Grant No. 2005B178) and the Center for Translational Molecular Medicine (Eminence project).
II. MATERIALS AND METHODS
II.B. Image acquisition
II.D. Parameter estimation
II.E. Model performance
II.F. Image quality
III.A. Model-based approach: Optimization
III.B. Model-based approach: Detection of microvascular differences
III.B.1. Optimal model
III.B.2. Extended GKM
III.C. Model-free approach: Optimization
III.D. Model-free approach: Comparison with model-based parameters
III.E. Image quality
IV.A. Current findings
IV.B. Optimization of model-based approach
IV.C. Comparison between model-free and model-based parameters
IV.D. Detection of microvascular differences
IV.F. MCAs in DCE-MRI
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