Spatially adaptive unsupervised multispectral nonlocal filtering for improved cerebral blood flow mapping using...

Cerebral blood flow (CBF) is an emerging biomarker for normal aging and neurodegenerative diseases. Arterial spin labeling (ASL) perfusion MRI permits noninvasive quantification of CBF. However, high-quality mapping of CBF from ASL imaging is challenging, largely due to noise.

We demonstrate the ability of the recently introduced nonlocal estimation of multispectral magnitudes (NESMA) filter to greatly improve determination of CBF estimates from ASL imaging data. We evaluated the results of NESMA-ASL for CBF mapping from data obtained on human brain (n = 10) across a wide age range (21-74 years) using a standard clinical protocol. Results were compared to those obtained from unfiltered images or filtered images using conventional and advanced filters. Quantitative analyses for different spatial image resolutions and signal-to-noise ratios, SNRs, were also conducted.

Our results demonstrate the potential of NESMA-ASL to permit high-quality high-resolution CBF mapping. NESMA-ASL substantially reduces random variation in derived CBF estimates while preserving edges and small structures, with minimal bias and dispersion in derived CBF estimates.

NESMA-ASL outperforms all evaluated filters in terms of noise reduction and detail preservation. Further, unlike other filters, NESMA-ASL is straightforward to implement requiring only one user-defined parameter, which is relatively insensitive to SNR or local image structure.

In-vivo estimation of CBF in the human brain from ASL imaging data was markedly improved through use of the NESMA-ASL filter. The use of NESMA-ASL may contribute significantly to the goal of high-quality high-resolution CBF mapping within a clinically feasible acquisition time.