  Deep learning-based image reconstruction approaches have demonstrated
impressive empirical performance in many imaging modalities. These approaches
usually require a large amount of high-quality paired training data, which is
often not available in medical imaging. To circumvent this issue we develop a
novel unsupervised knowledge-transfer paradigm for learned reconstruction
within a Bayesian framework. The proposed approach learns a reconstruction
network in two phases. The first phase trains a reconstruction network with a
set of ordered pairs comprising of ground truth images of ellipses and the
corresponding simulated measurement data. The second phase fine-tunes the
pretrained network to more realistic measurement data without supervision. By
construction, the framework is capable of delivering predictive uncertainty
information over the reconstructed image. We present extensive experimental
results on low-dose and sparse-view computed tomography showing that the
approach is competitive with several state-of-the-art supervised and
unsupervised reconstruction techniques. Moreover, for test data distributed
differently from the training data, the proposed framework can significantly
improve reconstruction quality not only visually, but also quantitatively in
terms of PSNR and SSIM, when compared with learned methods trained on the
synthetic dataset only.
