TV-constrained incremental algorithms for low-intensity CT image reconstruction

Low-dose X-ray computed tomography (CT) has garnered much recent interest as it provides a method to lower patient dose and simultaneously reduce scan time. In non-medical applications the possibility of preventing sample damage makes low-dose CT desirable. Reconstruction in low-dose CT poses a significant challenge due to the high level of noise in the data.

Here we propose an iterative method for reconstruction which minimizes the transmission Poisson likelihood subject to a total-variation constraint. This formulation accommodates efficient methods of parameter selection because the choice of TV constraint can be guided by an image reconstructed by filtered backprojection (FBP).

We apply our algorithm to low-dose synchrotron X-ray CT data from the Advanced Photon Source (APS) at Argonne National Labs (ANL) to demonstrate its potential utility. We find that the algorithm provides a means of edge-preserving regularization with the potential to generate useful images at low iteration number in low-dose CT.