  Surface analysis of the cortex is ubiquitous in human neuroimaging with MRI,
e.g., for cortical registration, parcellation, or thickness estimation. The
convoluted cortical geometry requires isotropic scans (e.g., 1mm MPRAGEs) and
good gray-white matter contrast for 3D reconstruction. This precludes the
analysis of most brain MRI scans acquired for clinical purposes. Analyzing such
scans would enable neuroimaging studies with sample sizes that cannot be
achieved with current research datasets, particularly for underrepresented
populations and rare diseases. Here we present the first method for cortical
reconstruction, registration, parcellation, and thickness estimation for
clinical brain MRI scans of any resolution and pulse sequence. The methods has
a learning component and a classical optimization module. The former uses
domain randomization to train a CNN that predicts an implicit representation of
the white matter and pial surfaces (a signed distance function) at 1mm
isotropic resolution, independently of the pulse sequence and resolution of the
input. The latter uses geometry processing to place the surfaces while
accurately satisfying topological and geometric constraints, thus enabling
subsequent parcellation and thickness estimation with existing methods. We
present results on 5mm axial FLAIR scans from ADNI and on a highly
heterogeneous clinical dataset with 5,000 scans. Code and data are publicly
available at https://surfer.nmr.mgh.harvard.edu/fswiki/recon-all-clinical
