Noise-Aware Standard-Dose PET Reconstruction Using General and Adaptive Robust Loss

[1]  Jonathan T. Barron,et al.  A General and Adaptive Robust Loss Function , 2017, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Kangfu Mei,et al.  Multi-scale Residual Network for Image Super-Resolution , 2018, ECCV.

[3]  Dinggang Shen,et al.  3D conditional generative adversarial networks for high-quality PET image estimation at low dose , 2018, NeuroImage.

[4]  Dinggang Shen,et al.  Deep auto-context convolutional neural networks for standard-dose PET image estimation from low-dose PET/MRI , 2017, Neurocomputing.

[5]  Kyoung Mu Lee,et al.  Enhanced Deep Residual Networks for Single Image Super-Resolution , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[6]  Alexei A. Efros,et al.  Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[7]  Alexei A. Efros,et al.  Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Christian Ledig,et al.  Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Lei Zhang,et al.  Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising , 2016, IEEE Transactions on Image Processing.

[10]  G. V. von Schulthess,et al.  Clinical evaluation of a block sequential regularized expectation maximization reconstruction algorithm in 18F-FDG PET/CT studies , 2017, Nuclear medicine communications.

[11]  Yan Wang,et al.  Multi-Level Canonical Correlation Analysis for Standard-Dose PET Image Estimation , 2016, IEEE Transactions on Image Processing.

[12]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Yan Wang,et al.  Multi-Level Canonical Correlation Analysis for Standard-Dose PET Image Estimation. , 2016, IEEE transactions on image processing : a publication of the IEEE Signal Processing Society.

[14]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[15]  R. Boellaard,et al.  Experimental and clinical evaluation of iterative reconstruction (OSEM) in dynamic PET: quantitative characteristics and effects on kinetic modeling. , 2001, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.