Lin et al., 2019 - Google Patents
Deep learning for low-field to high-field MR: image quality transfer with probabilistic decimation simulatorLin et al., 2019
View PDF- Document ID
- 11034864691940756801
- Author
- Lin H
- Figini M
- Tanno R
- Blumberg S
- Kaden E
- Ogbole G
- Brown B
- D’Arco F
- Carmichael D
- Lagunju I
- Cross H
- Fernandez-Reyes D
- Alexander D
- Publication year
- Publication venue
- Machine Learning for Medical Image Reconstruction: Second International Workshop, MLMIR 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings 2
External Links
Snippet
MR images scanned at low magnetic field (< 1 T) have lower resolution in the slice direction and lower contrast, due to a relatively small signal-to-noise ratio (SNR) than those from high field (typically 1.5 T and 3T). We adapt the recent idea of Image Quality Transfer (IQT) to …
- 238000005070 sampling 0 abstract description 12
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
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10084—Hybrid tomography; Concurrent acquisition with multiple different tomographic modalities
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R33/00—Arrangements or instruments for measuring magnetic variables
- G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
- G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
- G01R33/48—NMR imaging systems
- G01R33/54—Signal processing systems, e.g. using pulse sequences, Generation or control of pulse sequences ; Operator Console
- G01R33/56—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution
-
- 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
- G06T2207/10088—Magnetic resonance imaging [MRI]
-
- 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
- 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
- G06T3/00—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image
- G06T3/40—Scaling the whole image or part thereof
- G06T3/4053—Super resolution, i.e. output image resolution higher than sensor resolution
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2211/00—Image generation
- G06T2211/40—Computed tomography
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Zhou et al. | Handbook of medical image computing and computer assisted intervention | |
Korkmaz et al. | Unsupervised MRI reconstruction via zero-shot learned adversarial transformers | |
Usman et al. | Retrospective motion correction in multishot MRI using generative adversarial network | |
Zhu et al. | Image reconstruction by domain-transform manifold learning | |
Oh et al. | Unpaired deep learning for accelerated MRI using optimal transport driven CycleGAN | |
Lin et al. | Deep learning for low-field to high-field MR: image quality transfer with probabilistic decimation simulator | |
Dar et al. | Prior-guided image reconstruction for accelerated multi-contrast MRI via generative adversarial networks | |
Hammernik et al. | Physics-driven deep learning for computational magnetic resonance imaging: Combining physics and machine learning for improved medical imaging | |
Sim et al. | Optimal transport driven CycleGAN for unsupervised learning in inverse problems | |
Lee et al. | Deep learning in MR image processing | |
Zhang et al. | PTNet3D: A 3D high-resolution longitudinal infant brain MRI synthesizer based on transformers | |
Lam et al. | Constrained magnetic resonance spectroscopic imaging by learning nonlinear low-dimensional models | |
Liu et al. | Fusing multi-scale information in convolution network for MR image super-resolution reconstruction | |
Yurt et al. | Progressively volumetrized deep generative models for data-efficient contextual learning of MR image recovery | |
Lv et al. | Which GAN? A comparative study of generative adversarial network-based fast MRI reconstruction | |
Du et al. | Accelerated super-resolution MR image reconstruction via a 3D densely connected deep convolutional neural network | |
Lin et al. | Low-field magnetic resonance image enhancement via stochastic image quality transfer | |
Meng et al. | Accelerating T2 mapping of the brain by integrating deep learning priors with low‐rank and sparse modeling | |
Falvo et al. | A multimodal dense u-net for accelerating multiple sclerosis mri | |
Wang et al. | Inversesr: 3d brain mri super-resolution using a latent diffusion model | |
Thurnhofer-Hemsi et al. | Deep learning-based super-resolution of 3D magnetic resonance images by regularly spaced shifting | |
Song et al. | Deep robust residual network for super-resolution of 2D fetal brain MRI | |
US20230380714A1 (en) | Method and system for low-field mri denoising with a deep complex-valued convolutional neural network | |
Zhou et al. | Spatial orthogonal attention generative adversarial network for MRI reconstruction | |
Forigua et al. | Superformer: Volumetric transformer architectures for mri super-resolution |