Optimal acquisition and modeling parameters for accurate assessment of low K_trans blood-brain barrier permeability using dynamic contrast-enhanced MRI

Purpose To determine optimal parameters for acquisition and processing of dynamic contrast-enhanced MRI (DCE-MRI) to detect small changes in near normal low blood-brain barrier (BBB) permeability. Methods Using a contrast-to-noise ratio metric (K-CNR) for K_trans precision and accuracy, the effects of kinetic model selection, scan duration, temporal resolution, signal drift, and length of baseline on the estimation of low permeability values was evaluated with simulations. Results The Patlak model was shown to give the highest K-CNR at low K_trans. The K_trans transition point, above which other models yielded superior results, was highly dependent on scan duration and tissue extravascular extracellular volume fraction (v_e). The highest K-CNR for low K_trans was obtained when Patlak model analysis was combined with long scan times (10-30 min), modest temporal resolution (<60 s/image), and long baseline scans (1-4 min). Signal drift as low as 3% was shown to affect the accuracy of K_trans estimation with Patlak analysis. Conclusion DCE acquisition and modeling parameters are interdependent and should be optimized together for the tissue being imaged. Appropriately optimized protocols can detect even the subtlest changes in BBB integrity and may be used to probe the earliest changes in neurodegenerative diseases such as Alzheimer's disease and multiple sclerosis.