Comparison of three physiologically-based pharmacokinetic models for the prediction of contrast agent distribution measured by dynamic MR imaging.
PURPOSE: To compare the performance of three physiologically-based pharmacokinetic (PBPK) models for predicting gadolinium contrast agent concentration-time curves (Gd-CTCs) obtained in superior sagittal sinus (SSS), cerebral cortex, and psoas muscle. MATERIALS AND METHODS: Three published whole-body PBPK models were modified to predict Gd-CTCs in normal-appearing tissue. The models differed in the number of organs modeled and total number of compartments, and were designated as the "well-mixed," "delay," and "dispersion" models. The suitability of the three models to predict Gd-CTC was studied using data from dynamic contrast-enhanced MR perfusion imaging obtained at 1.5T from 10 patients with glioblastoma multiforme and at 3.0T from five patients with liver metastases. RESULTS: The dispersion model produced better fits than the delay model in the SSS (P < 0.0001) and cerebral cortex (P < 0.0001), and better fits than the well-mixed model in psoas muscle (P < 0.005). No model produced better fits than the dispersion model at any of the three locations. CONCLUSION: In this evaluation, the dispersion model was most robust for prediction of Gd-CTCs derived from dynamic contrast-enhanced (DCE)-MRI. This represents a preliminary step in the development of a PBPK model useful for predicting Gd-CTCs at a time resolution appropriate for dynamic MRI applications.
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Related Subject Headings
- Tissue Distribution
- Superior Sagittal Sinus
- Psoas Muscles
- Predictive Value of Tests
- Nuclear Medicine & Medical Imaging
- Models, Biological
- Middle Aged
- Male
- Magnetic Resonance Imaging
- Liver Neoplasms
Citation
Published In
DOI
ISSN
Publication Date
Volume
Issue
Start / End Page
Location
Related Subject Headings
- Tissue Distribution
- Superior Sagittal Sinus
- Psoas Muscles
- Predictive Value of Tests
- Nuclear Medicine & Medical Imaging
- Models, Biological
- Middle Aged
- Male
- Magnetic Resonance Imaging
- Liver Neoplasms