USLR: an open-source tool for unbiased and smooth longitudinal
  registration of brain MR
Adrià Casamitjana Roser Sala-Llonch Karim Lekadir Juan Eugenio Iglesias
We present USLR, a computational framework for longitudinal registration of
brain MRI scans to estimate nonlinear image trajectories that are smooth across
time, unbiased to any timepoint, and robust to imaging artefacts. It operates
on the Lie algebra parameterisation of spatial transforms (which is compatible
with rigid transforms and stationary velocity fields for nonlinear deformation)
and takes advantage of log-domain properties to solve the problem using
Bayesian inference. USRL estimates rigid and nonlinear registrations that: (i)
bring all timepoints to an unbiased subject-specific space; and (i) compute a
smooth trajectory across the imaging time-series. We capitalise on
learning-based registration algorithms and closed-form expressions for fast
inference. A use-case Alzheimer's disease study is used to showcase the
benefits of the pipeline in multiple fronts, such as time-consistent image
segmentation to reduce intra-subject variability, subject-specific prediction
or population analysis using tensor-based morphometry. We demonstrate that such
approach improves upon cross-sectional methods in identifying group
differences, which can be helpful in detecting more subtle atrophy levels or in
reducing sample sizes in clinical trials. The code is publicly available in
https://github.com/acasamitjana/uslr