Enabling Clinical Decisions From Low-power MRI In Developing Nations Through Image Quality Transfer

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The long-term vision motivating this project is of software solutions that enable low-power cheap-and-sustainable imaging devices able to provide point-of-care image data in resource-poor locations at diagnostic/prognostic quality. We achieve this by propagating information from databases of high quality images. We provide a proof of concept using MRI from lower-power scanners available in LMICs, specifically Nigeria, that we enhance by propagating information from databases of images from state-of-the-art MRI scanners available in the UK. We focus on an application to childhood epilepsy to demonstrate early clinical benefit. Childhood epilepsy presents an immediate clinical need in LMICs, as MRI from widely available 0.36T scanners is insufficient to support clinical decisions on curative surgery that are routinely made in the UK using 1.5T or 3T images. This leaves many patients untreated, living with severe epilepsy and resulting physical disabilities and mental disorders, unable to work effectively, and draining sparse medical and social-care resources.We draw on the latest advances in machine learning to approximate the MRIs available in the UK from those accessible in the paediatric neurology clinic in UCH Ibadan, Nigeria - a typical sub-Saharan city hospital. Machine learning has made major advances over the last few years. In particular, it shows remarkable feats of artificial intelligence in data-rich application areas such as computer vision where, for example, computers now outperform humans in object recognition. The advances are just starting to make an impact in medical imaging, which presents unique challenges because a) less data is available than many non-medical computer vision tasks, b) decisions are often more critical as they impact directly on patient outcome. Our recent image quality transfer (IQT) framework propagates information from high quality to low quality medical images. It shows compelling early results, such as revealing thin white matter pathways, usually only accessible from specialist high resolution data sets, from standard resolution images acquired on a clinical scanner. Here we advance IQT to exploit the latest machine learning techniques, enhance those techniques to provide confidence measures valuable for medical decision-making, and tailor solutions specifically to enhance images from the Ibadan paediatric clinic with those from similar cohorts in the UK. We acquire and collate the data sets sufficient to support learning the required image-to-image mappings. Matched pairs of images from the same subjects from UK and Nigerian scanners are not practical to obtain, so we employ unsupervised and semi-supervised learning to construct image-to-image mappings without directly matching training data. We refine promising implementations and assess their impact on clinical decision making in a pilot study in Ibadan using locally agreed metrics. We intend this project as a springboard for a much wider and long term program exploring these ideas to bring about a paradigm shift in imaging that deploys cheap point-of-care devices built specifically to acquire data enhanced by databases of high quality images acquired on state of the art or bespoke devices.