  Traditional model-based image reconstruction (MBIR) methods combine forward
and noise models with simple object priors. Recent application of deep learning
methods for image reconstruction provides a successful data-driven approach to
addressing the challenges when reconstructing images with measurement
undersampling or various types of noise. In this work, we propose a hybrid
supervised-unsupervised learning framework for X-ray computed tomography (CT)
image reconstruction. The proposed learning formulation leverages both sparsity
or unsupervised learning-based priors and neural network reconstructors to
simulate a fixed-point iteration process. Each proposed trained block consists
of a deterministic MBIR solver and a neural network. The information flows in
parallel through these two reconstructors and is then optimally combined, and
multiple such blocks are cascaded to form a reconstruction pipeline. We
demonstrate the efficacy of this learned hybrid model for low-dose CT image
reconstruction with limited training data, where we use the NIH AAPM Mayo
Clinic Low Dose CT Grand Challenge dataset for training and testing. In our
experiments, we study combinations of supervised deep network reconstructors
and sparse representations-based (unsupervised) learned or analytical priors.
Our results demonstrate the promising performance of the proposed framework
compared to recent reconstruction methods.
