Optimal Acquisition and Modeling Parameters for Accurate Assessment of Low K_(trans) Blood–Brain Barrier Permeability Using Dynamic Contrast-Enhanced MRI

Abstract

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.

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