  Low-dose tomography is highly preferred in medical procedures for its reduced
radiation risk when compared to standard-dose Computed Tomography (CT).
However, the lower the intensity of X-rays, the higher the acquisition noise
and hence the reconstructions suffer from artefacts. A large body of work has
focussed on improving the algorithms to minimize these artefacts. In this work,
we propose two new techniques, rescaled non-linear least squares and
Poisson-Gaussian convolution, that reconstruct the underlying image making use
of an accurate or near-accurate statistical model of the noise in the
projections. We also propose a reconstruction method when prior knowledge of
the underlying object is available in the form of templates. This is applicable
to longitudinal studies wherein the same object is scanned multiple times to
observe the changes that evolve in it over time. Our results on 3D data show
that prior information can be used to compensate for the low-dose artefacts,
and we demonstrate that it is possible to simultaneously prevent the prior from
adversely biasing the reconstructions of new changes in the test object, via a
method called ``re-irradiation''. Additionally, we also present two techniques
for automated tuning of the regularization parameters for tomographic
inversion.
