Nothing Special   »   [go: up one dir, main page]

Emami et al., 2018 - Google Patents

Generating synthetic CTs from magnetic resonance images using generative adversarial networks

Emami et al., 2018

View PDF
Document ID
9963074223619316714
Author
Emami H
Dong M
Nejad‐Davarani S
Glide‐Hurst C
Publication year
Publication venue
Medical physics

External Links

Snippet

Purpose While MR‐only treatment planning using synthetic CTs (synCTs) offers potential for streamlining clinical workflow, a need exists for an efficient and automated synCT generation in the brain to facilitate near real‐time MR‐only planning. This work describes a …
Continue reading at aapm.onlinelibrary.wiley.com (PDF) (other versions)

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
    • G06T2207/10084—Hybrid tomography; Concurrent acquisition with multiple different tomographic modalities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/32—Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
    • 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
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • 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
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • 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
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—2D [Two Dimensional] image generation
    • 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

Similar Documents

Publication Publication Date Title
Emami et al. Generating synthetic CTs from magnetic resonance images using generative adversarial networks
Wang et al. A review on medical imaging synthesis using deep learning and its clinical applications
Han MR‐based synthetic CT generation using a deep convolutional neural network method
Bahrami et al. A new deep convolutional neural network design with efficient learning capability: Application to CT image synthesis from MRI
Wang et al. 3D auto-context-based locality adaptive multi-modality GANs for PET synthesis
Harms et al. Paired cycle‐GAN‐based image correction for quantitative cone‐beam computed tomography
Ouyang et al. Ultra‐low‐dose PET reconstruction using generative adversarial network with feature matching and task‐specific perceptual loss
Lei et al. MRI‐only based synthetic CT generation using dense cycle consistent generative adversarial networks
Zhu et al. AnatomyNet: deep learning for fast and fully automated whole‐volume segmentation of head and neck anatomy
Dai et al. Multimodal MRI synthesis using unified generative adversarial networks
Wolterink et al. Deep MR to CT synthesis using unpaired data
Liu SUSAN: segment unannotated image structure using adversarial network
Staring et al. A rigidity penalty term for nonrigid registration
Emami et al. SA-GAN: Structure-aware GAN for organ-preserving synthetic CT generation
Osman et al. Deep learning‐based convolutional neural network for intramodality brain MRI synthesis
Chen et al. Deep learning based unpaired image-to-image translation applications for medical physics: a systematic review
Apostolopoulos et al. Applications of generative adversarial networks (GANs) in positron emission tomography (PET) imaging: A review
Zhou et al. 3D segmentation guided style-based generative adversarial networks for pet synthesis
Amirkolaee et al. Development of a GAN architecture based on integrating global and local information for paired and unpaired medical image translation
Gheorghiță et al. Improving robustness of automatic cardiac function quantification from cine magnetic resonance imaging using synthetic image data
Jiang et al. Deep cross‐modality (MR‐CT) educed distillation learning for cone beam CT lung tumor segmentation
Hassanaly et al. Unsupervised anomaly detection in 3D brain FDG PET: A benchmark of 17 VAE-based approaches
Vajpayee et al. Structurally-constrained optical-flow-guided adversarial generation of synthetic CT for MR-only radiotherapy treatment planning
Lee et al. Study on Optimal Generative Network for Synthesizing Brain Tumor‐Segmented MR Images
Amirkolaee et al. Medical image translation using an edge-guided generative adversarial network with global-to-local feature fusion