From Event: SPIE Optical Engineering + Applications, 2017
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.
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 (Presented at SPIE Optical Engineering + Applications: August 08, 2017; Published: 19 September 2017); https://doi.org/10.1117/12.2272723.
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