  This paper explores convolutional generative networks as an alternative to
iterative reconstruction algorithms in medical image reconstruction. The task
of medical image reconstruction involves mapping of projection main data
collected from the detector to the image domain. This mapping is done typically
through iterative reconstruction algorithms which are time consuming and
computationally expensive. Trained deep learning networks provide faster
outputs as proven in various tasks across computer vision. In this work we
propose a direct reconstruction framework exclusively with deep learning
architectures. The proposed framework consists of three segments, namely
denoising, reconstruction and super resolution. The denoising and the super
resolution segments act as processing steps. The reconstruction segment
consists of a novel double U-Net generator (DUG) which learns the
sinogram-to-image transformation. This entire network was trained on positron
emission tomography (PET) and computed tomography (CT) images. The
reconstruction framework approximates two-dimensional (2-D) mapping from
projection domain to image domain. The architecture proposed in this
proof-of-concept work is a novel approach to direct image reconstruction;
further improvement is required to implement it in a clinical setting.
