Dynamic contrast-enhanced MRI of the patellar bone: How to quantify perfusion
Journal of Magnetic Resonance Imaging , Volume 47 - Issue 3 p. 848- 858
Purpose: To identify the optimal combination of pharmacokinetic model and arterial input function (AIF) for quantitative analysis of blood perfusion in the patellar bone using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Materials and Methods: This method design study used a random subset of five control subjects from an Institutional Review Board (IRB)-approved case–control study into patellofemoral pain, scanned on a 3T MR system with a contrast-enhanced time-resolved imaging of contrast kinetics (TRICKS) sequence. We systematically investigated the reproducibility of pharmacokinetic parameters for all combinations of Orton and Parker AIF models with Tofts, Extended Tofts (ETofts), and Brix pharmacokinetic models. Furthermore, we evaluated if the AIF should use literature parameters, be subject-specific, or group-specific. Model selection was based on the goodness-of-fit and the coefficient of variation of the pharmacokinetic parameters inside the patella. This extends previous studies that were not focused on the patella and did not evaluate as many combinations of arterial and pharmacokinetic models. Results: The vascular component in the ETofts model could not reliably be recovered (coefficient of variation [CV] of vp >50%) and the Brix model parameters showed high variability of up to 20% for kel across good AIF models. Compared to group-specific AIF, the subject-specific AIF's mostly had higher residual. The best reproducibility and goodness-of-fit were obtained by combining Tofts' pharmacokinetic model with the group-specific Parker AIF. Conclusion: We identified several good combinations of pharmacokinetic models and AIF for quantitative analysis of perfusion in the patellar bone. The recommended combination is Tofts pharmacokinetic model combined with a group-specific Parker AIF model. Level of Evidence: 2. Technical Efficacy: Stage 1. J. Magn. Reson. Imaging 2018;47:848–858.