Volumetric analyses using 3D T1-weighted acquisitions (3D-T1) has become an integral part of the clinical work-up for neurodegenerative diseases [1,2], epilepsy [3, http://hipposeg.cs.ucl.ac.uk], and many clinical trials with both cross-sectional and longitudinal designs [4]. A major confound in volumetric analysis is acquisition protocol variation – most notably between vendors but also between specific models, pulse sequences, and parameter choices – compromising the integration of imaging-derived biomarkers across sites and their use for the monitoring and management of patients scanned with different machines. In this work we evaluate the variations in T1 image quality and volumetry across six 3 T MRI scanners – from three main vendors – at a single site. This is a crucial step towards the standardisation of these sequences to improve their translation to clinical use and their harmonisation between centres.Two healthy subjects (28yo male, 46yo female) were scanned on six 3 T scanners: a Philips Achieva, GE MR750, and Siemens Skyra, Trio, Prisma, and Biograph mMR (PET-MR). Each subject was scanned three times on all scanners. To discriminate potentially larger day-to-day variation from same-day scan-rescan changes [5], each subject underwent two scans on one day with a five-minute interval between scans and a further single scan on a separate day on each scanner. All protocols are the established standard sequences used in routine clinical practice and/or research studies, optimised for different dedicated applications and patient populations. Acquisition details for each scanner are given in Table 1. Scans were registered to an intermediate space to visualise contrast differences between protocols. Tissue segmentations were performed using Geodesic Image Flows framework (GIF [6]) in native space, which includes bias-field correction, from which total intracranial volume (TIV), WM, cortical (cGM) and deep (dGM) grey matter, and ventricular and non-ventricular CSF volumes were extracted. Signal-to-noise ratios were quantified within each tissue type by calculating mean and variance over the tissue, and the CNR between two tissue types was defined as the absolute difference in mean values between those two tissues divided by the weighted mean of the variances.Fig. 1 shows equivalent axial and coronal slices for all six scanners, in which contrast differences are evident between the six scanners. The clearest difference is in the GM-WM contrast between the scanners. The protocol on the Achieva has the highest visual GM-WM contrast, arguably at the cost of lower cGM-CSF contrast – as confirmed by the estimated CNR (Table 2). By contrast, images from the Siemens scanners tend to have higher GM-CSF contrast. Volumetric analyses are shown in Figs. 2 and 3. Maximum difference in TIV between scanners is around 60 ml, or just under 4%, where maximum intra-scanner TIV variation is well under 1%. Variation in TIV across scanners is consistent across subjects. The differences in contrast (Fig. 1) result in pronounced variations in segmented tissue volume – even when normalised by TIV. Most notably, the Biograph mMR yields WM volumes 1% higher than the other scanners, and the MR750 yields cGM estimates around 1-1.5% higher. In both cases there is no linked and equal decrease in adjacent tissues, indicating that it is not one tissue interface that is classified differently. Intra-scanner variation is low on all scanners for WM, cGM, dGM, and non-ventricular CSF, with the lateral ventricles showing the largest inter-scan variability.Inter-scanner variation in volumetric brain analysis is larger than intra-scanner variation. Differences in TIV are possibly indicative of slight gradient calibration variations, leading to minor scaling differences between the true head dimensions and each scanner’s representation thereof. However, estimated tissue volumes, normalised for TIV, still vary more across scanners than within. Slight inter-scanner imaging parameter variations may be the cause of this. Sequence implementation differences across vendors might also contribute to between-scanner variation, and the relatively close clustering of volume estimates across the Siemens Skyra, Trio, and Prisma systems, relative to those from the other scanners, suggests vendor-based differences – although the Siemens Biograph mMR shows a large deviation in WM volumes compared to all other scanners. With respect to CNR estimates, the vendors employ different inline or optional image reconstruction filters which might bias the comparison across vendors. Future work will extend this study to a larger subject group and investigate further metrics including localised analyses and cortical thickness.