  We present an algorithm for creating high resolution anatomically plausible
images consistent with acquired clinical brain MRI scans with large inter-slice
spacing. Although large data sets of clinical images contain a wealth of
information, time constraints during acquisition result in sparse scans that
fail to capture much of the anatomy. These characteristics often render
computational analysis impractical as many image analysis algorithms tend to
fail when applied to such images. Highly specialized algorithms that explicitly
handle sparse slice spacing do not generalize well across problem domains. In
contrast, we aim to enable application of existing algorithms that were
originally developed for high resolution research scans to significantly
undersampled scans. We introduce a generative model that captures fine-scale
anatomical structure across subjects in clinical image collections and derive
an algorithm for filling in the missing data in scans with large inter-slice
spacing. Our experimental results demonstrate that the resulting method
outperforms state-of-the-art upsampling super-resolution techniques, and
promises to facilitate subsequent analysis not previously possible with scans
of this quality. Our implementation is freely available at
https://github.com/adalca/papago .
