The huge number of hospital MRI examinations routinely obtained for clinical purposes is a potentially valuable “big data” resource alongside established genetic and phenotypic data streams for largescale experimental neurology. However, acquisition variation may compromise the research value of clinical imaging data: radiology units addressing a complex range of diagnostic questions commonly employ many indication-specific protocols differing in detail between operators, scanners, and institutions. A first step towards reducing this variation is to systematically capture the sequence variants and their frequency of use. To this end, we developed an automated method to capture long-term MRI sequence-variant usage statistics in routine practice across multiple scanners at our neuroradiology center, aiming to quantify protocol variation and identify a ‘core’ suite of the most-used sequences to facilitate acquisition protocol harmonization.Using an in-house developed software tool [1] we parsed the system scanning logs of four clinical MRI scanners (Siemens Magnetom: 1.5T Avanto, 1.5T Espree, 3T Skyra, and 3T Trio). Individual scanner records consisted of ~12K measurements over a ~ 4 months period (Sep 2014 – Jan 2015), and for one system (1.5T Avanto) we analysed data from a longer period (June2013- Oct 2015) ~60K measurements. Heuristic tests identified individual pulse-sequence execution occurrences, recording user-defined MRI sequence names to provide a machine-generated statistical record of pulse-sequence variant use. The user-defined sequence name strings include the core contrast (e.g. ‘t2_tse’) with additional characters denoting sequence variants, employed for specific indications with, generally, small variations in acquisition parameters. Head and neck only acquisitions were included, identified by cross-referencing scanner protocol tree paths and RF coil identification, and localiser sequences were excluded. Final data reduction was performed in Microsoft Excel (V.14.07).The most frequently used sequences were ascribed to our group of core-contrast clinical sequences. For each of these we determined the frequency of execution of respective sequence-variants.Across 4 scanners, a total of ~5060 patient examinations were performed during the ~4 months period. Among the most frequently deployed were variants of the following: 2D multi-slice pulse sequences: T2 weighted (T2w) turbo-spin echo (TSE), T2w FLAIR, T1w FLASH, T1w spin-echo, and diffusion weighted DWI; and 3D volume sequences T1w MPRAGE and 3D SWI -FLASH. Figures 1-2 show histograms of sequence-variant execution frequency for representative 1.5T and 3T scanners. Across our 4 scanners, the most frequently deployed basic sequences were common, but there were differences in their relative execution frequencies. Figures 3-4 show the number of sequence-variants for each of the core-contrast sequences for 1.5T and 3T scanners respectively. In general there were more sequence-variants on our 1.5T than 3T systems, with T2w TSE and T1w SE being deployed with the largest number of variations. For 2D acquisitions, scan orientation choices increased the number of variants (e.g. on the Avanto 1.5T, for the T1_SE core contrast there were 16 axial, 25 coronal and 6 sagittal variants, and for T2_TSE - 36 axial, 11 coronal and 4 sagittal). Among the 3D sequences, there were more MPRAGE variants than SWI FLASH for both field strengths.A statistical approach to scanner activity analysis revealed the most commonly used core “menu” of sequences used in clinical brain examination. For all scanners, groups of the same most frequently used sequences were observed, differences between their relative frequencies presumably reflecting scanner-dependent differences in the clinical workflow. However, sequence variations may also occur for historical or arbitrary reasons: this data collection will permit inspection of the sequence variants to determine if they truly reflect indication-specific requirements, or can be standardized to reduce data variation. A bespoke system analytics tool enables the capture of MRI acquisition statistics not practical by manual record keeping, and a means to derive ongoing service quality control data. We hypothesize that harmonization of clinical MRI protocols across sites/hospitals will increase the value of clinical MRI data as a large-scale research resource, with decreased acquisition variation helping to improve the statistical power of future large-scale patient-data studies.