Background: Brain morphometry is extensively used in cross-sectional studies. However, the difference in the\r\nestimated values of the morphometric measures between patients and healthy subjects may be small and hence\r\novershadowed by the scanner-related variability, especially with multicentre and longitudinal studies. It is important\r\ntherefore to investigate the variability and reliability of morphometric measurements between different scanners\r\nand different sessions of the same scanner.\r\nMethods: We assessed the variability and reliability for the grey matter, white matter, cerebrospinal fluid and\r\ncerebral hemisphere volumes as well as the global sulcal index, sulcal surface and mean geodesic depth using\r\nBrainvisa. We used datasets obtained across multiple MR scanners at 1.5 T and 3 T from the same groups of 13\r\nand 11 healthy volunteers, respectively. For each morphometric measure, we conducted ANOVA analysis and\r\nverified whether the estimated values were significantly different across different scanners or different sessions of\r\nthe same scanner. The between-centre and between-visit reliabilities were estimated from their contribution to the\r\ntotal variance, using a random-effects ANOVA model. To estimate the main processes responsible for low reliability,\r\nthe results of brain segmentation were compared to those obtained using FAST within FSL.\r\nResults: In a considerable number of cases, the main effects of both centre and visit factors were found to be\r\nsignificant. Moreover, both between-centre and between-visit reliabilities ranged from poor to excellent for most\r\nmorphometric measures. A comparison between segmentation using Brainvisa and FAST revealed that FAST\r\nimproved the reliabilities for most cases, suggesting that morphometry could benefit from improving the bias\r\ncorrection. However, the results were still significantly different across different scanners or different visits.\r\nConclusions: Our results confirm that for morphometry analysis with the current version of Brainvisa using data\r\nfrom multicentre or longitudinal studies, the scanner-related variability must be taken into account and where\r\npossible should be corrected for. We also suggest providing some flexibility to Brainvisa for a step-by-step analysis\r\nof the robustness of this package in terms of reproducibility of the results by allowing the bias corrected images to\r\nbe imported from other packages and bias correction step be skipped, for example.
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