Generative Adversarial Networks for Noise Reduction in Low-Dose CT

By Jelmer M. Wolterink, Tim Leiner, Max A. Viergever, Ivana Isgum. 2017

915 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Lifelong Generative Modeling (2017, background): “It has wide ranging applications from image denoising [4, 5], inpainting [6, 7], 5”
Generative Adversarial Network in Medical Imaging: A Review (2018, methodology): “The basic pix2pix framework has been used for low dose CT denoising (Wolterink et al., 2017b), MR reconstruction (Chen et al., 2018b; Kim et al., 2018; Dar et al., 2018b; Shitrit and Raviv, 2017), and PET denoising (Wang et al., 2018b).”
MedGAN: Medical Image Translation using GANs (2018, methodology): “This method was used previously for various medical applications such as MR to CT translation [46], CT denoising [48] and”
3D conditional generative adversarial networks for high-quality PET image estimation at low dose (2018, methodology): “Different from (Wolterink et al., 2017), we employ the L1 norm instead of L2 distance to encourage less blurring.”
Hi-Net: Hybrid-Fusion Network for Multi-Modal MR Image Synthesis (2020, methodology): “[18] utilized GAN to transform low-dose CT into routine-dose CT images.”
Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment (2020, methodology): “GANs have successfully been applied to image-to-image translation [58], segmentation [59] and many other subfields of medical image computing [60].”
Global Guarantees for Enforcing Deep Generative Priors by Empirical Risk (2017, background): “These more sophisticated priors are recently emerging in empirical applications of many fields of imaging, such as medical imaging [70, 30, 24, 55, 17, 1, 49, 68, 53, 43, 32, 42, 16, 69], microscopy [59], inpainting [45, 71], superresolution [60, 33, 28], compressed sensing [38, 37, 51, 50], image m”
Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training (2017, methodology): “GANs have been used for noise reduction in low-dose CT in [28] and for MR to CT prediction in [29].”
Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss (2017, background): “Although CNN-VGG and WGAN-VGG visually share a similar result, the quantitative analysis shows WGAN-VGG enjoys higher PSNRs and more faithful statistical properties of denoised images relative to those of NDCT images.”
3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network (2018, neutral): “The adversarial loss (AL) in the GAN framework could result in a sharp image locally indistinguishable from the NDCT image but it does not exactly match the NDCT 1This work cascades several CNNs.”
Deep MR to CT Synthesis Using Unpaired Data (2017, background): “Possible applications are synthesis between MR images acquired at diﬀerent ﬁeld strengths [1], or between CT images acquired at diﬀerent dose levels [10].”
Deep Generative Adversarial Neural Networks for Compressive Sensing (GANCS) MRI (2018, background): “There is a very recent study on using GANs in [31] for denoising CT images where the generator is a deep CNN and”
Chest x-ray generation and data augmentation for cardiovascular abnormality classification (2018, background): “There have been specialized uses such as for image denoising [12].”
Sharpness-Aware Low-Dose CT Denoising Using Conditional Generative Adversarial Network (2017, methodology): “It is unclear howmuch dose reduction was achieved for the other deep learning based approaches [13, 33, 65, 67].”
The evolution of image reconstruction for CT—from filtered back projection to artificial intelligence (2018, methodology): “One application is image-space-based reconstructions in which convolutional neural networks are trained with low-dose CT images to reconstruct routine-dose CT images [17, 114, 115].”