  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.
