Neuroharmony’s documentation

The increasing availability of magnetic resonance imaging (MRI) datasets is boosting the interest in the application of machine learning in neuroimaging. A key challenge to the development of reliable machine learning models, and their translational implementation in real-word clinical practice, is the integration of datasets collected using different scanners. Current approaches for harmonizing multi-scanner data, such as the ComBat method, require a statistical representative sample, and therefore are not suitable for machine learning models aimed at clinical translation where the focus is on the assessment of individual scans from previously unseen scanners. To overcome this challenge, Neuroharmony uses image quality metrics (i.e. intrinsic characteristics which can be extracted from individual images without requiring a statistical representative sample and any extra information about the scanners) to harmonize single images from unseen/unknown scanners based on.