  Neutron Computed Tomography (CT) is an increasingly utilised non-destructive
analysis tool in material science, palaeontology, and cultural heritage. With
the development of new neutron imaging facilities (such as DINGO, ANSTO,
Australia) new opportunities arise to maximise their performance through the
implementation of statistically driven image reconstruction methods which have
yet to see wide scale application in neutron transmission tomography. This work
outlines the implementation of a convex algorithm statistical image
reconstruction framework applicable to the geometry of most neutron tomography
instruments with the aim of obtaining similar imaging quality to conventional
ramp filtered back-projection via the inverse Radon transform, but using a
lower number of measured projections to increase object throughput. Through
comparison of the output of these two frameworks using a tomographic scan of a
known 3 material cylindrical phantom obtain with the DINGO neutron radiography
instrument (ANSTO, Australia), this work illustrates the advantages of
statistical image reconstruction techniques over conventional filter
back-projection. It was found that the statistical image reconstruction
framework was capable of obtaining image estimates of similar quality with
respect to filtered back-projection using only 12.5% the number of projections,
potentially increasing object throughput at neutron imaging facilities such as
DINGO eight-fold.
