Minghui Zhang
ABSTRACT
Reducing low-dose radiation while maintaining high-quality image reconstruction in X-ray computed tomography (CT) is challenging, due to the reconstruction images degradation as the number of projection view decreases. As opposed to most of the existing deep network approaches with supervised learning scheme, which requires the data in the learning and testing procedure to be the same dimension, in the paper we propose an unsupervised learning approach based on the denoising autoencoding prior (DAEP) for few-view CT reconstruction. Two innovations were done to substantially improve the naive DAEP. First, by employing the virtual variables technique, higher-dimensional network is learned and then incorporated into the single-channel reconstruction procedure. Second, replacing the L2 regression loss function by more robust L1 regression is favor to preserve texture details. Experimental results demonstrate that the proposed method can achieve promising effects over state-of-the-art methods in terms of peak signal-to-noise ratio (PSNR) and subjective visual quality.
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Index Terms
- Sparse-View CT Reconstruction via Robust and Multi-channels Autoencoding Priors
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