  Multi-energy CT takes advantage of the non-linearly varying attenuation
properties of elemental media with respect to energy, enabling more precise
material identification than single-energy CT. The increased precision comes
with the cost of a higher radiation dose. A straightforward way to lower the
dose is to reduce the number of projections per energy, but this makes
tomographic reconstruction more ill-posed. In this paper, we propose how this
problem can be overcome with a combination of a regularization method that
promotes structural similarity between images at different energies and a
suitably selected low-dose data acquisition protocol using non-overlapping
projections. The performance of various joint regularization models is assessed
with both simulated and experimental data, using the novel low-dose data
acquisition protocol. Three of the models are well-established, namely the
joint total variation, the linear parallel level sets and the spectral
smoothness promoting regularization models. Furthermore, one new joint
regularization model is introduced for multi-energy CT: a regularization based
on the structure function from the structural similarity index. The findings
show that joint regularization outperforms individual channel-by-channel
reconstruction. Furthermore, the proposed combination of joint reconstruction
and non-overlapping projection geometry enables significant reduction of
radiation dose.
