PurposeArterial spin labeling (ASL) acquisitions at multiple post‐labeling delays may provide more accurate quantification of cerebral blood flow (CBF), by fitting appropriate kinetic models and simultaneously estimating relevant parameters such as the arterial transit time (ATT) and arterial cerebral blood volume (aCBV). We evaluate the effects of denoising strategies on model fitting and parameter estimation when accounting for the dispersion of the label bolus through the vasculature in cerebrovascular disease.MethodsWe analyzed multi‐delay ASL data from 17 cerebral small vessel disease patients (50 ± 9 y) and 13 healthy controls (52 ± 8 y), by fitting an extended kinetic model with or without bolus dispersion. We considered two denoising strategies: removal of structured noise sources by independent component analysis (ICA) of the control‐label image timeseries; and averaging the repetitions of the control‐label images prior to model fitting.ResultsModeling bolus dispersion improved estimation precision and impacted parameter values, but these effects strongly depended on whether repetitions were averaged before model fitting. In general, repetition averaging improved model fitting but adversely affected parameter values, particularly CBF and aCBV near arterial locations in patients. This suggests that using all repetitions allows better noise estimation at the earlier delays. In contrast, ICA denoising improved model fitting and estimation precision while leaving parameter values unaffected.ConclusionOur results support the use of ICA denoising to improve model fitting to multi‐delay ASL and suggest that using all control‐label repetitions improves the estimation of macrovascular signal contributions and hence perfusion quantification near arterial locations. This is important when modeling flow dispersion in cerebrovascular pathology.
Purpose Histogram-based metrics extracted from diffusion-tensor imaging (DTI) have been suggested as potential biomarkers for cerebral small vessel disease (SVD), but methods and results have varied across studies. This work aims to assess the impact of mask selection for extracting histogram-based metrics of fractional anisotropy (FA) and mean diffusivity (MD) on their sensitivity as SVD biomarkers.Methods DTI data were collected from 17 SVD patients and 12 healthy controls. For each participant, FA and MD maps were estimated; from these, histograms were computed on two alternative whole-brain white-matter masks: normal-appearing white-matter (NAWM) and mean FA tract skeleton (TBSS). Histogram-based metrics (median, peak height, peak width, peak value) were extracted from the FA and MD maps. These were compared between patients and controls, and correlated with the patients’ cognitive scores (executive function and processing speed).Results White matter mask selection significantly impacted FA and MD histogram metrics and affected their ability to discriminate between groups. Moreover, we observed that the mask can influence the correlations with cognitive measures. Nevertheless, the MD peak height and MD peak width metrics remained significantly correlated with executive function, regardless of the mask.Conclusion Our results corroborate previous reports and further support the value of DTI histogram-based metrics as SVD biomarkers. However, they also highlight the importance of the processing methodology, in particular the choice of white matter mask, as hence the urgent need to mitigate the lack of standardized MRI data-processing pipelines.
Sliding window Pearson correlation (SW) is the most commonly used approach for estimating dynamic functional connectivity (dFC). However, instantaneous phase coherence (PC) has gained popularity as it yields frame-by-frame dFC estimates. This work aimed to compare both metrics by analysing the mean lifetime, probability of occurrence and spatial similarity of dFC states with the canonical resting-state networks (RSNs). We found that the state lifetimes increase in SW compared to PC and with window length, worsening the detection of RSNs for smaller datasets. These findings indicate that the temporal blurring induced by SW compromises the ability to detect faster network dynamics.
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