Reproducibility, reliability and variability of FA and MD in the older healthy population: A test-retest multiparametric analysis

Evaluating High Spatial Resolution Diffusion Kurtosis Imaging at 3T: Reproducibility and Quality of Fit

“…For the GM, we focused on the five lobes and determined the average DKI CoV across cortical depths (ie, from WM to the pial surface). Although DKI reproducibility was lower in GM compared to whole‐brain WM, potentially due to partial volume effects, 29,30 a similar trend was noticed between dataset A and dataset B, both achieving CoVs ranging from 10–15%. Dataset C had higher interscan variability across the five lobes in all the maps.…”

Comparison of structural MRI brain measures between 1.5 and 3 T: Data from the Lothian Birth Cohort 1936

“…In one of the largest between-scanner comparisons to date, we report previously lacking information on a wide range of structural brain measures in an exclusively older group of participants. We found excellent levels of consistency (ICC >~.75) between the 1.5 and 3 T scanners for the largest brain structures (whole-brain, ventricular and tissue volumes; global dMRI measures in WM; and global network metrics) that were similar to same-scanner test-retest studies (Buchanan et al, 2014;Iscan et al, 2015;Luque Laguna et al, 2020;Melzer et al, 2020). We noted that there were overall mean shifts in the absolute levels of most measures between 1.5 and 3 T: volumetric measures and thickness appeared larger at 3 T, RD, and MD were lower, and AD and FA were higher at 3 T, consistent with prior observations from smaller studies on single metrics (Chu et al, 2017;Han et al, 2006;Heinen et al, 2016;Pfefferbaum et al, 2012), but not others (West et al, 2013).…”

“…Agreement is generally lower for specific regional volumetric measures, where cortical thickness ICCs >~.50 have been reported (Madan & Kensinger, 2017), and ICCs >~.8 even when both acquisitions were taken in the same session (Liem et al, 2015). Similarly, tract-or region-specific diffusion measures have been reported with ICCs < .54 (Luque Laguna et al, 2020), ICCs > .72 (Boekel, Forstmann, & Keuken, 2017), and coefficient of variation (CoV) <10% (Clayden, Storkey, Maniega, & Bastin, 2009). This trend is echoed by structural connectomic measures, in which global network properties had more reliable (but still imperfect) test-retest agreement (ICCs > .6), in contrast to lower reliability (ICCs >~.5) of regional/nodal properties (Buchanan, Pernet, Gorgolewski, Storkey, & Bastin, 2014;Cheng et al, 2012).…”

“…When considering the potential for two different field strengths to yield different estimates of the same brain measurements, interpretation should be tempered by the finding that even same-scanner measurements, taken twice or more over short periods, are not perfectly reliable. Same-scanner test-retest studies of the same subjects report agreement as low as .8 in terms of the intraclass correlation coefficient (ICC) for global volumetric and water diffusion measures (Iscan et al, 2015;Luque Laguna et al, 2020;Melzer et al, 2020).…”

Precision Diffusion Imaging

“…PFM has revealed individual variants in functional network architecture that went undetected with typical amounts of data per subject [22][23][24][25] By analogy, intensive acquisition of DWIs in individuals could be similarly fruitful in the study of structural brain connectivity. Earlier studies have examined reliability and accuracy in diffusion imaging studies using less than 60 diffusion directions [26,27], by comparing mean FA [16,28,29], reliability of tract-averaged FA [30,31], and capacity to resolve crossing-fiber models [32,33]. However, it is unclear what degree of within-individual reliability could be achieved by collecting much larger quantities of DTI data.…”