  Low Dose Computed Tomography suffers from a high amount of noise and/or
undersampling artefacts in the reconstructed image. In the current article, a
Deep Learning technique is exploited as a regularization term for the iterative
reconstruction method SIRT. While SIRT minimizes the error in the sinogram
space, the proposed regularization model additionally steers intermediate SIRT
reconstructions towards the desired output. Extensive evaluations demonstrate
the superior outcomes of the proposed method compared to the state of the art
techniques. Comparing the forward projection of the reconstructed image with
the original signal shows a higher fidelity to the sinogram space for the
current approach amongst other learning based methods.
