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

Low radiation tomographic reconstruction with and without template information