  Neutron computed tomography (nCT) is a 3D characterization technique used to
image the internal morphology or chemical composition of samples in biology and
materials sciences. A typical workflow involves placing the sample in the path
of a neutron beam, acquiring projection data at a predefined set of
orientations, and processing the resulting data using an analytic
reconstruction algorithm. Typical nCT scans require hours to days to complete
and are then processed using conventional filtered back-projection (FBP), which
performs poorly with sparse views or noisy data. Hence, the main methods in
order to reduce overall acquisition time are the use of an improved sampling
strategy combined with the use of advanced reconstruction methods such as
model-based iterative reconstruction (MBIR). In this paper, we propose an
adaptive orientation selection method in which an MBIR reconstruction on
previously-acquired measurements is used to define an objective function on
orientations that balances a data-fitting term promoting edge alignment and a
regularization term promoting orientation diversity. Using simulated and
experimental data, we demonstrate that our method produces high-quality
reconstructions using significantly fewer total measurements than the
conventional approach.
