Crop Filling: a pipeline for repairing memory clinic MRI corrupted by partial brain coverage

Data-driven solutions offer great promise for improving healthcare. However standard clinical neuroimaging data is subject to real-world imaging artefacts that can render the data unusable for computational research. T1 weighted structural MRI is used in research to obtain volumetric measurements from cortical and subcortical brain regions. However, clinical radiologists often prioritise T2 weighted or FLAIR scans for visual assessment. As such, T1 weighted scans are often acquired but may not be a priority. This can result in artefacts such as partial brain coverage being systematically present in memory clinic data. Here we present a neuroimaging pipeline to ameliorate such situations by filling the missing regions with synthetic data. We validate on artificially cropped scans from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), showing that our pipeline largely removes the artefact, improving volumetric biomarker accuracy while also retaining statistical differences between diagnostic groups. We demonstrate utility by achieving diagnostic classification performance comparable to uncorrupted data. This is an important contribution towards moving research from the lab into the real world.