2018
DOI: 10.1016/j.jneumeth.2018.08.018
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Spatially adaptive unsupervised multispectral nonlocal filtering for improved cerebral blood flow mapping using arterial spin labeling magnetic resonance imaging

Abstract: 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.

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Cited by 12 publications
(9 citation statements)
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“… 29 Finally, a whole-brain CBF map was generated from the pCASL data set using the NESMA-ASL analysis to improve accuracy and precision in CBF determination. 24 28 …”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“… 29 Finally, a whole-brain CBF map was generated from the pCASL data set using the NESMA-ASL analysis to improve accuracy and precision in CBF determination. 24 28 …”
Section: Methodsmentioning
confidence: 99%
“…CBF was measured using the nonlocal estimation of multispectral magnitudes (NESMA)-ASL analysis for accurate and precise CBF determination. 24 Our goal is to characterise the regional associations between cortical or subcortical CBF and local myelination variations in critical white matter regions.…”
Section: Introductionmentioning
confidence: 99%
“…The total acquisition time was ~12 minutes. Other details of this protocol can be found in the literature 24 . A whole‐brain CBF map was then generated from the pCASL dataset using the NESMA‐ASL analysis to improve accuracy and precision in CBF determination 23,24 …”
Section: Methodsmentioning
confidence: 99%
“…An experiment with no labeling was additionally conducted with 5 repetitions. The nonlocal estimation of multispectral magnitudes (NESMA) filter [ 38 ] was applied to the averaged images and a region of interest (ROI) spanning both the right and left hippocampus was manually defined with reference to a corresponding proton density-weighted image (Fig. 2 b).…”
Section: Methodsmentioning
confidence: 99%