Huang et al., 2020 - Google Patents
CaGAN: A cycle-consistent generative adversarial network with attention for low-dose CT imagingHuang et al., 2020
- Document ID
- 10448973671731593174
- Author
- Huang Z
- Chen Z
- Zhang Q
- Quan G
- Ji M
- Zhang C
- Yang Y
- Liu X
- Liang D
- Zheng H
- Hu Z
- Publication year
- Publication venue
- IEEE Transactions on Computational Imaging
External Links
Snippet
Although lowering X-ray radiation helps reduce the risk of cancer in patients, low-dose computed tomography (LDCT) technology usually leads to poor image quality, such as amplified mottle noise and streak artifacts, which severely impact the diagnostic results. To …
- 238000003384 imaging method 0 title description 33
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10104—Positron emission tomography [PET]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20048—Transform domain processing
- G06T2207/20064—Wavelet transform [DWT]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10024—Color image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
- G06T5/001—Image restoration
- G06T5/002—Denoising; Smoothing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
- G06T11/003—Reconstruction from projections, e.g. tomography
- G06T11/006—Inverse problem, transformation from projection-space into object-space, e.g. transform methods, back-projection, algebraic methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10116—X-ray image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2211/00—Image generation
- G06T2211/40—Computed tomography
- G06T2211/424—Iterative
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
- G06T5/007—Dynamic range modification
- G06T5/008—Local, e.g. shadow enhancement
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
- G06T5/20—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image by the use of local operators
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Huang et al. | CaGAN: A cycle-consistent generative adversarial network with attention for low-dose CT imaging | |
He et al. | Optimizing a parameterized plug-and-play ADMM for iterative low-dose CT reconstruction | |
Yin et al. | Domain progressive 3D residual convolution network to improve low-dose CT imaging | |
Yang et al. | Low-dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss | |
Lu et al. | Iterative reconstruction of low-dose CT based on differential sparse | |
Heinrich et al. | Residual U-net convolutional neural network architecture for low-dose CT denoising | |
Hu et al. | Hybrid-domain neural network processing for sparse-view CT reconstruction | |
Liu et al. | Low-dose CT restoration via stacked sparse denoising autoencoders | |
CN108898642B (en) | Sparse angle CT imaging method based on convolutional neural network | |
Ye et al. | Deep back projection for sparse-view CT reconstruction | |
Ko et al. | Rigid and non-rigid motion artifact reduction in X-ray CT using attention module | |
Tao et al. | VVBP-tensor in the FBP algorithm: its properties and application in low-dose CT reconstruction | |
Chen et al. | Bone suppression of chest radiographs with cascaded convolutional networks in wavelet domain | |
Li et al. | Incorporation of residual attention modules into two neural networks for low‐dose CT denoising | |
Pan et al. | Iterative residual optimization network for limited-angle tomographic reconstruction | |
Hou et al. | CT image quality enhancement via a dual-channel neural network with jointing denoising and super-resolution | |
Li et al. | Low-dose computed tomography image reconstruction via a multistage convolutional neural network with autoencoder perceptual loss network | |
Xia et al. | Deep residual neural network based image enhancement algorithm for low dose CT images | |
Wang et al. | Limited-angle computed tomography reconstruction using combined FDK-based neural network and U-Net | |
Liu et al. | MRCON-Net: Multiscale reweighted convolutional coding neural network for low-dose CT imaging | |
Li et al. | A comprehensive survey on deep learning techniques in CT image quality improvement | |
Wu et al. | Masked joint bilateral filtering via deep image prior for digital X-ray image denoising | |
Chen et al. | Deep learning-based algorithms for low-dose CT imaging: A review | |
Poonkodi et al. | 3D-MedTranCSGAN: 3D medical image transformation using CSGAN | |
Zhang et al. | Weighted tensor low-rankness and learnable analysis sparse representation model for texture preserving low-dose CT reconstruction |