Relative sensitivities of DCE-MRI pharmacokinetic parameters to arterial input function (AIF) scaling

Dynamic-Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) has been used widely for clinical applications. Pharmacokinetic modeling of DCE-MRI data that extracts quantitative contrast reagent/tissue-specific model parameters is the most investigated method. One of the primary challenges in pharmacokinetic analysis of DCE-MRI data is accurate and reliable measurement of the arterial input function (AIF), which is the driving force behind all pharmacokinetics. Because of effects such as inflow and partial volume averaging, AIF measured from individual arteries sometimes require amplitude scaling for better representation of the blood contrast reagent (CR) concentration time-courses. Empirical approaches like blinded AIF estimation or reference tissue AIF derivation can be useful and practical, especially when there is no clearly visible blood vessel within the imaging field-of-view (FOV). Similarly, these approaches generally also require magnitude scaling of the derived AIF time-courses. Since the AIF varies among individuals even with the same CR injection protocol and the perfect scaling factor for reconstructing the ground truth AIF often remains unknown, variations in estimated pharmacokinetic parameters due to varying AIF scaling factors are of special interest. In this work, using simulated and real prostate cancer DCE-MRI data, we examined parameter variations associated with AIF scaling. Our results show that, for both the fast-exchange-limit (FXL) Tofts model and the water exchange sensitized fast-exchange-regime (FXR) model, the commonly fitted CR transfer constant (K^trans) and the extravascular, extracellular volume fraction (v_e) scale nearly proportionally with the AIF, whereas the FXR-specific unidirectional cellular water efflux rate constant, k_io, and the CR intravasation rate constant, k_ep, are both AIF scaling insensitive. This indicates that, for DCE-MRI of prostate cancer and possibly other cancers, k_io and k_ep may be more suitable imaging biomarkers for cross-platform, multicenter applications. Data from our limited study cohort show that k_io correlates with Gleason scores, suggesting that it may be a useful biomarker for prostate cancer disease progression monitoring.