Joint reconstruction in low dose multi-energy CT

Jussi Toivanen, ,Department of Applied Physics, University of Eastern Finland, POB 1627, FI-70211 Kuopio, Finland, Alexander Meaney, Samuli Siltanen, Ville Kolehmainen, ,Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
2020 Inverse Problems and Imaging  
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
more » ... 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. 2010 Mathematics Subject Classification. Primary: 92C55, 65N21; Secondary: 65Z05.
doi:10.3934/ipi.2020028 fatcat:224gvzfrvrfhxbrs2oo4zzi2fm