A Tool for Super-Resolving Multimodal Clinical MRI
This tool addresses the problem of extracting intelligence from clinical MRI data for healthcare applications, but it appears incremental as it builds on existing model-based techniques.
The authors tackled the challenge of automated processing of multimodal clinical MRI images, which exhibit high variability, by developing a tool that uses a generative model to super-resolve thick-sliced images without manual tuning or training. They demonstrated that the tool outperforms conventional methods on simulated data and successfully processes a large hospital dataset.
We present a tool for resolution recovery in multimodal clinical magnetic resonance imaging (MRI). Such images exhibit great variability, both biological and instrumental. This variability makes automated processing with neuroimaging analysis software very challenging. This leaves intelligence extractable only from large-scale analyses of clinical data untapped, and impedes the introduction of automated predictive systems in clinical care. The tool presented in this paper enables such processing, via inference in a generative model of thick-sliced, multi-contrast MR scans. All model parameters are estimated from the observed data, without the need for manual tuning. The model-driven nature of the approach means that no type of training is needed for applicability to the diversity of MR contrasts present in a clinical context. We show on simulated data that the proposed approach outperforms conventional model-based techniques, and on a large hospital dataset of multimodal MRIs that the tool can successfully super-resolve very thick-sliced images. The implementation is available from https://github.com/brudfors/spm_superres.