Unsupervised Knowledge-Transfer for Learned Image Reconstruction

• URL: http://arxiv.org/abs/2107.02572v1
• Date: Tue, 6 Jul 2021 12:19:16 GMT
• Title: Unsupervised Knowledge-Transfer for Learned Image Reconstruction
• Authors: Riccardo Barbano, Zeljko Kereta, Andreas Hauptmann, Simon R. Arridge, Bangti Jin
• Abstract summary: We develop a novel unsupervised knowledge-transfer paradigm for learned iterative reconstruction within a Bayesian framework. We show that the proposed framework significantly improves reconstruction quality not only visually, but also quantitatively in terms of PSNR and SSIM.
• Score: 4.183935970343543
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning-based image reconstruction approaches have demonstrated impressive empirical performance in many imaging modalities. These approaches generally require a large amount of high-quality training data, which is often not available. To circumvent this issue, we develop a novel unsupervised knowledge-transfer paradigm for learned iterative reconstruction within a Bayesian framework. The proposed approach learns an iterative reconstruction network in two phases. The first phase trains a reconstruction network with a set of ordered pairs comprising of ground truth images and measurement data. The second phase fine-tunes the pretrained network to the measurement data without supervision. Furthermore, the framework delivers uncertainty information over the reconstructed image. We present extensive experimental results on low-dose and sparse-view computed tomography, showing that the proposed framework significantly improves reconstruction quality not only visually, but also quantitatively in terms of PSNR and SSIM, and is competitive with several state-of-the-art supervised and unsupervised reconstruction techniques.

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