Low-Dose CT Image Reconstruction using Vector Quantized Convolutional Autoencoder with Perceptual Loss
Author:
Publisher
Springer Science and Business Media LLC
Subject
Multidisciplinary
Link
https://link.springer.com/content/pdf/10.1007/s12046-023-02107-1.pdf
Reference19 articles.
1. Xu Q, Yu H, Mou X, Zhang L, Hsieh J and Wang G 2012 Low-dose X-ray CT reconstruction via dictionary learning. IEEE Trans. Med. Imaging, 31: 1682–1697
2. Chen H, Zhang Y, Kalra M K, Lin F. Chen Y, Liao P, Zhou J and Wang G 2017 Low-dose CT with a residual encoder-decoder convolutional neural network. IEEE Trans. Med. Imaging, 36: 2524–2535
3. Li Q, Li S, Li R, Wu W, Dong Y, Zhao J, Qiang Y and Aftab R 2022 Low-dose computed tomography image reconstruction via a multistage convolutional neural network with autoencoder perceptual loss network. Quant. Imaging Med. Surg., 12: 1929
4. McCollough C 2016 TU–FG–207A–04: an overview of the low dose CT grand challenge. Med. Phys., 43: 3759–3760
5. Leuschner J, Schmidt M, Baguer D O and Maass P 2021 LoDoPaB-CT a benchmark dataset for low-dose computed tomography reconstruction. Sci. Data, 8: 109
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