  Most existing algorithms for automatic 3D morphometry of human brain MRI
scans are designed for data with near-isotropic voxels at approximately 1 mm
resolution, and frequently have contrast constraints as well - typically
requiring T1 scans (e.g., MP-RAGE). This limitation prevents the analysis of
millions of MRI scans acquired with large inter-slice spacing ("thick slice")
in clinical settings every year. The inability to quantitatively analyze these
scans hinders the adoption of quantitative neuroimaging in healthcare, and
precludes research studies that could attain huge sample sizes and hence
greatly improve our understanding of the human brain. Recent advances in CNNs
are producing outstanding results in super-resolution and contrast synthesis of
MRI. However, these approaches are very sensitive to the contrast, resolution
and orientation of the input images, and thus do not generalize to diverse
clinical acquisition protocols - even within sites. Here we present SynthSR, a
method to train a CNN that receives one or more thick-slice scans with
different contrast, resolution and orientation, and produces an isotropic scan
of canonical contrast (typically a 1 mm MP-RAGE). The presented method does not
require any preprocessing, e.g., skull stripping or bias field correction.
Crucially, SynthSR trains on synthetic input images generated from 3D
segmentations, and can thus be used to train CNNs for any combination of
contrasts, resolutions and orientations without high-resolution training data.
We test the images generated with SynthSR in an array of common downstream
analyses, and show that they can be reliably used for subcortical segmentation
and volumetry, image registration (e.g., for tensor-based morphometry), and, if
some image quality requirements are met, even cortical thickness morphometry.
The source code is publicly available at github.com/BBillot/SynthSR.
