Improving subtle BBB permeability estimation using iterative nonlocal estimation of multispectral magnitudes (iNESMA) filtering

Motivation: Recently, Dynamic Contrast-Enhanced MRI studies revealed increased Blood-Brain Barrier (BBB) permeability in aging and in Alzheimer’s disease (AD). However, the subtle BBB disruption in aging and in AD yields substantially low contrast extravasation, which results in an intrinsically low signal-to-noise ratio. Goal(s): An effective filtering method is desirable to suppress noise, while maintaining the spatial variation in contrast dynamics. Approach: We propose an iterative nonlocal estimation of multispectral magnitudes (iNESMA) filtering approach, which achieves noise-filtering by combining the voxels with similar spectral patterns. Results: Our results suggest that iNESMA filtering allows accurate and precise determination of kinetic parameters for subtle BBB permeability. Impact: We propose an effective, yet straightforward, filtering paradigm for improved determination of the kinetic parameters from DCE-MR images. Our proposed iNESMA filtering would allow better characterization of subtle vascular changes in aging and in AD.