Deep learning for low-dose CT

Hu Chen; Yi Zhang; Jiliu Zhou; Ge Wang

doi:10.1117/12.2272723

19 September 2017 Deep learning for low-dose CT

Hu Chen, Yi Zhang, Jiliu Zhou, Ge Wang

Proceedings Volume 10391, Developments in X-Ray Tomography XI; 103910I (2017) https://doi.org/10.1117/12.2272723
Event: SPIE Optical Engineering + Applications, 2017, San Diego, California, United States

Abstract

Given the potential risk of X-ray radiation to the patient, low-dose CT has attracted a considerable interest in the medical imaging field. Currently, the main stream low-dose CT methods include vendor-specific sinogram domain filtration and iterative reconstruction algorithms, but they need to access raw data whose formats are not transparent to most users. Due to the difficulty of modeling the statistical characteristics in the image domain, the existing methods for directly processing reconstructed images cannot eliminate image noise very well while keeping structural details. Inspired by the idea of deep learning, here we combine the autoencoder, deconvolution network, and shortcut connections into the residual encoder-decoder convolutional neural network (RED-CNN) for low-dose CT imaging. After patch-based training, the proposed RED-CNN achieves a competitive performance relative to the-state-of-art methods. Especially, our method has been favorably evaluated in terms of noise suppression and structural preservation.

Conference Presentation

Citation Download Citation

Hu Chen, Yi Zhang, Jiliu Zhou, and Ge Wang "Deep learning for low-dose CT", Proc. SPIE 10391, Developments in X-Ray Tomography XI, 103910I (19 September 2017); https://doi.org/10.1117/12.2272723

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