  In this paper we present a generalized Deep Learning-based approach for
solving ill-posed large-scale inverse problems occuring in medical image
reconstruction. Recently, Deep Learning methods using iterative neural networks
and cascaded neural networks have been reported to achieve state-of-the-art
results with respect to various quantitative quality measures as PSNR, NRMSE
and SSIM across different imaging modalities. However, the fact that these
approaches employ the forward and adjoint operators repeatedly in the network
architecture requires the network to process the whole images or volumes at
once, which for some applications is computationally infeasible. In this work,
we follow a different reconstruction strategy by decoupling the regularization
of the solution from ensuring consistency with the measured data. The
regularization is given in the form of an image prior obtained by the output of
a previously trained neural network which is used in a Tikhonov regularization
framework. By doing so, more complex and sophisticated network architectures
can be used for the removal of the artefacts or noise than it is usually the
case in iterative networks. Due to the large scale of the considered problems
and the resulting computational complexity of the employed networks, the priors
are obtained by processing the images or volumes as patches or slices. We
evaluated the method for the cases of 3D cone-beam low dose CT and undersampled
2D radial cine MRI and compared it to a total variation-minimization-based
reconstruction algorithm as well as to a method with regularization based on
learned overcomplete dictionaries. The proposed method outperformed all the
reported methods with respect to all chosen quantitative measures and further
accelerates the regularization step in the reconstruction by several orders of
magnitude.
