Unsupervised Knowledge-Transfer for Learned Image Reconstruction
This addresses the data scarcity issue in medical imaging reconstruction, offering an incremental advancement over existing unsupervised and supervised techniques.
The paper tackles the problem of requiring large paired datasets for deep learning-based image reconstruction in medical imaging by developing an unsupervised knowledge-transfer paradigm within a Bayesian framework, showing competitive results with state-of-the-art methods and significant improvements in PSNR and SSIM for out-of-distribution test data.
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.