[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Mai et al., 2020 - Google Patents

Metal Artifacts Reduction in CT Scans using Convolutional Neural Network with Ground Truth Elimination

Mai et al., 2020

View PDF
Document ID
17957571667524792649
Author
Mai Q
Wan J
Publication year
Publication venue
2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

External Links

Snippet

Metal artifacts are very common in CT scans since metal insertion or replacement is performed for enhancing certain functionality or mechanism of patient's body. These streak artifacts could degrade CT image quality severely, and consequently, they could influence …
Continue reading at cs.uwaterloo.ca (PDF) (other versions)

Classifications

    • 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
    • G06T11/005—Specific pre-processing for tomographic reconstruction, e.g. calibration, source positioning, rebinning, scatter correction, retrospective gating
    • 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
    • G06T2207/10116—X-ray image
    • 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
    • G06T2207/20212—Image combination
    • 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
    • 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
    • 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
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G06T7/0014—Biomedical image inspection using an image reference approach
    • 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
    • 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
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20172—Image enhancement details
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
    • A61B6/52—Devices using data or image processing specially adapted for radiation diagnosis
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
    • A61B6/50—Clinical applications
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
    • A61B6/48—Diagnostic techniques

Similar Documents

Publication Publication Date Title
US11727569B2 (en) Training a CNN with pseudo ground truth for CT artifact reduction
Zhang et al. Convolutional neural network based metal artifact reduction in X-ray computed tomography
US11120582B2 (en) Unified dual-domain network for medical image formation, recovery, and analysis
CN106920246B (en) Uncertainty map for segmentation in the presence of metal artifacts
Meyer et al. Frequency split metal artifact reduction (FSMAR) in computed tomography
CN106683144B (en) Image iterative reconstruction method and device
RU2605519C2 (en) Motion compensated second pass metal artifact correction for computed tomography slice images
Wang et al. DICDNet: deep interpretable convolutional dictionary network for metal artifact reduction in CT images
Zhou et al. DuDoUFNet: dual-domain under-to-fully-complete progressive restoration network for simultaneous metal artifact reduction and low-dose CT reconstruction
CN111429379B (en) Low-dose CT image denoising method and system based on self-supervision learning
US10311603B2 (en) Correcting positron emission tomography image attenuation
Du et al. A convolutional neural network based auto-positioning method for dental arch in rotational panoramic radiography
Stille et al. Augmented likelihood image reconstruction
CN111815735B (en) Human tissue self-adaptive CT reconstruction method and reconstruction system
KR102472464B1 (en) Image Processing Method and Image Processing Device using the same
Kim et al. Convolutional neural network–based metal and streak artifacts reduction in dental CT images with sparse‐view sampling scheme
Liu et al. Unsupervised CT metal artifact reduction by plugging diffusion priors in dual domains
Mai et al. Metal Artifacts Reduction in CT Scans using Convolutional Neural Network with Ground Truth Elimination
CN113570586A (en) Method and device for creating and processing CT image of neural network system
JP2024507767A (en) Training data synthesizer for contrast-corrected machine learning systems
Cui et al. Prior Knowledge-guided Triple-Domain Transformer-GAN for Direct PET Reconstruction from Low-Count Sinograms
KR102441033B1 (en) Deep-learning based limited-angle computed tomography image reconstruction system
CN116664429A (en) Semi-supervised method for removing metal artifacts in multi-energy spectrum CT image
Zubair et al. Enhancing Low-Dose CT Image Quality Through Deep Learning: A DoG-Sharpened U-Net Approach With Attention Mechanism
Gottschalk et al. Deep learning based metal inpainting in the projection domain: Initial results