3D conditional generative adversarial networks for high-quality PET image estimation at low dose
暂无分享,去创建一个
Dinggang Shen | Lei Wang | Yan Wang | Chen Zu | Luping Zhou | Jiliu Zhou | Weili Lin | Xi Wu | Biting Yu | David S. Lalush | Lei Wang | D. Lalush | Weili Lin | D. Shen | Jiliu Zhou | Yan Wang | Xi Wu | C. Zu | Luping Zhou | Biting Yu
[1] Mark W. Woolrich,et al. Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.
[2] E. Reiman,et al. Multicenter Standardized 18F-FDG PET Diagnosis of Mild Cognitive Impairment, Alzheimer's Disease, and Other Dementias , 2008, Journal of Nuclear Medicine.
[3] Jiajun Wu,et al. Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling , 2016, NIPS.
[4] Léon Bottou,et al. Towards Principled Methods for Training Generative Adversarial Networks , 2017, ICLR.
[5] Yan Wang,et al. Multi-Level Canonical Correlation Analysis for Standard-Dose PET Image Estimation , 2016, IEEE Transactions on Image Processing.
[6] Ghassan Hamarneh,et al. BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment , 2017, NeuroImage.
[7] J. Mazziotta,et al. Rapid Automated Algorithm for Aligning and Reslicing PET Images , 1992, Journal of computer assisted tomography.
[8] Soumith Chintala,et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.
[9] Dinggang Shen,et al. Deep auto-context convolutional neural networks for standard-dose PET image estimation from low-dose PET/MRI , 2017, Neurocomputing.
[10] H. Malcolm Hudson,et al. Accelerated image reconstruction using ordered subsets of projection data , 1994, IEEE Trans. Medical Imaging.
[11] Yaozong Gao,et al. Prediction of standard-dose brain PET image by using MRI and low-dose brain [18F]FDG PET images. , 2015, Medical physics.
[12] E. Karnabi,et al. Positron Emission Tomography , 2017 .
[13] Osslan Osiris Vergara-Villegas,et al. Noise Reduction in Small-Animal PET Images Using a Multiresolution Transform , 2014, IEEE Transactions on Medical Imaging.
[14] Rob Fergus,et al. Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks , 2015, NIPS.
[15] Stefano F. Cappa,et al. Brain metabolic maps in Mild Cognitive Impairment predict heterogeneity of progression to dementia , 2014, NeuroImage: Clinical.
[16] Léon Bottou,et al. Wasserstein GAN , 2017, ArXiv.
[17] Konstantinos Kamnitsas,et al. Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation , 2016, Medical Image Anal..
[18] David Berthelot,et al. BEGAN: Boundary Equilibrium Generative Adversarial Networks , 2017, ArXiv.
[19] Dimitris Visvikis,et al. Denoising of PET images by combining wavelets and curvelets for improved preservation of resolution and quantitation , 2013, Medical Image Anal..
[20] Alex Graves,et al. Conditional Image Generation with PixelCNN Decoders , 2016, NIPS.
[21] Dinggang Shen,et al. LABEL: Pediatric brain extraction using learning-based meta-algorithm , 2012, NeuroImage.
[22] David Dagan Feng,et al. Synthesis of Positron Emission Tomography (PET) Images via Multi-channel Generative Adversarial Networks (GANs) , 2017, CMMI/RAMBO/SWITCH@MICCAI.
[23] Torsten Rohlfing,et al. Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains , 2004, NeuroImage.
[24] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[25] 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).
[26] Jose Dolz,et al. 3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study , 2016, NeuroImage.
[27] P. Valk,et al. Positon emission tomography. Basic sciences , 2006 .
[28] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[29] Dinggang Shen,et al. Predicting Standard-Dose PET Image from Low-Dose PET and Multimodal MR Images Using Mapping-Based Sparse Representation , 2015, MLMI.
[30] Alexei A. Efros,et al. Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[31] Max A. Viergever,et al. Generative Adversarial Networks for Noise Reduction in Low-Dose CT , 2017, IEEE Transactions on Medical Imaging.
[32] Klaus H. Maier-Hein,et al. Deep MRI brain extraction: A 3D convolutional neural network for skull stripping , 2016, NeuroImage.
[33] Pieter Abbeel,et al. InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets , 2016, NIPS.
[34] Simon Osindero,et al. Conditional Generative Adversarial Nets , 2014, ArXiv.
[35] Georges El Fakhri,et al. Motion compensation for brain PET imaging using wireless MR active markers in simultaneous PET–MR: Phantom and non-human primate studies , 2014, NeuroImage.
[36] Ulas Bagci,et al. Denoising PET Images Using Singular Value Thresholding and Stein's Unbiased Risk Estimate , 2013, MICCAI.
[37] A. Danek,et al. Evaluation of early-phase [18F]-florbetaben PET acquisition in clinical routine cases , 2016, NeuroImage: Clinical.
[38] Bernt Schiele,et al. Generative Adversarial Text to Image Synthesis , 2016, ICML.
[39] Alex Rovira,et al. Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach , 2017, NeuroImage.
[40] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[41] Paul Kinahan,et al. Positron emission tomography-computed tomography standardized uptake values in clinical practice and assessing response to therapy. , 2010, Seminars in ultrasound, CT, and MR.
[42] Yann LeCun,et al. Energy-based Generative Adversarial Network , 2016, ICLR.
[43] Dinggang Shen,et al. Semisupervised Tripled Dictionary Learning for Standard-Dose PET Image Prediction Using Low-Dose PET and Multimodal MRI , 2017, IEEE Transactions on Biomedical Engineering.