Compresssed sensing seems to be very promising for image reconstruction in computed tomography. In the last
years it has been shown, that these algorithms are able to handle incomplete data sets quite well. As cost function
these algorithms use the l1-norm of the image after it has been transformed by a sparsifying transformation.
This yields to an inequality-constrained convex optimization problem.
Due to the large size of the optimization problem some heuristic optimization algorithms have been proposed
in the last years. The most popular way is optimizing the rawdata and sparsity cost functions separately in an
In this paper we will follow this strategy. Thereby we present a new method to adapt these optimization steps.
Compared to existing methods which perform similar, the proposed method needs no a priori knowledge about
the rawdata consistency. It is ensured that the algorithm converges to the best possible value of the rawdata cost
function, while holding the sparsity constraint at a low value. This is achieved by transferring both optimization
procedures into the rawdata domain, where they are adapted to each other.
To evaluate the algorithm, we process measured clinical datasets. To cover a wide field of possible applications, we
focus on the problems of angular undersampling, data lost due to metal implants, limited view angle tomography
and interior tomography. In all cases the presented method reaches convergence within less than 25 iteration
steps, while using a constant set of algorithm control parameters. The image artifacts caused by incomplete
rawdata are mostly removed without introducing new effects like staircasing. All scenarios are compared to an
existing implementation of the ASD-POCS algorithm, which realizes the stepsize adaption in a different way.
Additional prior information as proposed by the PICCS algorithm can be incorporated easily into the optimization