[go: up one dir, main page]

Choi et al., 2022 - Google Patents

Self-supervised inter-and intra-slice correlation learning for low-dose CT image restoration without ground truth

Choi et al., 2022

View HTML
Document ID
9707549337657572524
Author
Choi K
Lim J
Kim S
Publication year
Publication venue
Expert Systems with Applications

External Links

Snippet

Training a convolutional neural network (CNN) to reduce noise in low-dose CT (LDCT) images typically relies on supervised learning, which requires input–target pairs of noisy LDCT and corresponding full-dose CT (FDCT) images. Although previous approaches have …
Continue reading at www.sciencedirect.com (HTML) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10072Tomographic images
    • G06T2207/10104Positron emission tomography [PET]
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2211/00Image generation
    • G06T2211/40Computed tomography
    • G06T2211/424Iterative
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/30Subject of image; Context of image processing
    • G06T2207/30004Biomedical image processing
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10072Tomographic images
    • G06T2207/10084Hybrid tomography; Concurrent acquisition with multiple different tomographic modalities
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/002D [Two Dimensional] image generation
    • G06T11/003Reconstruction from projections, e.g. tomography
    • G06T11/005Specific pre-processing for tomographic reconstruction, e.g. calibration, source positioning, rebinning, scatter correction, retrospective gating
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/002D [Two Dimensional] image generation
    • G06T11/003Reconstruction from projections, e.g. tomography
    • G06T11/006Inverse problem, transformation from projection-space into object-space, e.g. transform methods, back-projection, algebraic methods
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/20Special algorithmic details
    • G06T2207/20048Transform domain processing
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
    • G06T5/001Image restoration
    • G06T5/002Denoising; Smoothing
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/20Special algorithmic details
    • G06T2207/20076Probabilistic image processing
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/0002Inspection of images, e.g. flaw detection
    • G06T7/0012Biomedical image inspection
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
    • G06T5/007Dynamic range modification
    • G06T5/008Local, e.g. shadow enhancement
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
    • A61B6/48Diagnostic techniques
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
    • A61B6/50Clinical applications
    • A61B6/507Clinical applications involving determination of haemodynamic parameters, e.g. perfusion CT
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
    • A61B6/02Devices for diagnosis sequentially in different planes; Stereoscopic radiation diagnosis
    • A61B6/03Computerised tomographs
    • A61B6/032Transmission computed tomography [CT]

Similar Documents

Publication Publication Date Title
JP7598496B2 (en) Neural network learning method, program, medical image processing method, and medical device
Gao et al. CoreDiff: Contextual error-modulated generalized diffusion model for low-dose CT denoising and generalization
JP7432356B2 (en) Medical equipment and programs
JP6855223B2 (en) Medical image processing device, X-ray computer tomographic imaging device and medical image processing method
Kang et al. Deep convolutional framelet denosing for low-dose CT via wavelet residual network
Choi et al. Self-supervised inter-and intra-slice correlation learning for low-dose CT image restoration without ground truth
US11126914B2 (en) Image generation using machine learning
Kuanar et al. Low dose abdominal CT image reconstruction: an unsupervised learning based approach
Yuan et al. SIPID: A deep learning framework for sinogram interpolation and image denoising in low-dose CT reconstruction
Zhang et al. Accurate and robust sparse‐view angle CT image reconstruction using deep learning and prior image constrained compressed sensing (DL‐PICCS)
US20030076988A1 (en) Noise treatment of low-dose computed tomography projections and images
JP2021013725A (en) Medical apparatus
US20240185485A1 (en) Machine learning-based improvement in iterative image reconstruction
Tao et al. VVBP-tensor in the FBP algorithm: its properties and application in low-dose CT reconstruction
US20240311974A1 (en) Contrast boost by machine learning
Li et al. Incorporation of residual attention modules into two neural networks for low‐dose CT denoising
Zhang et al. PET image reconstruction using a cascading back-projection neural network
Liu et al. Low-dose CT imaging via cascaded ResUnet with spectrum loss
CN115777114A (en) 3D-CNN processing for CT image denoising
Wu et al. Unsharp structure guided filtering for self-supervised low-dose CT imaging
Du et al. X-ray CT image denoising with MINF: A modularized iterative network framework for data from multiple dose levels
Katta et al. A Hybrid Approach for CT Image Noise Reduction Combining Method Noise-CNN and Shearlet Transform
Meng et al. DDT-Net: Dose-agnostic dual-task transfer network for simultaneous low-dose CT denoising and simulation
Mazandarani et al. Gradient-based optimization algorithm for hybrid loss function in low-dose ct denoising
Park et al. Low-dose CT image reconstruction with a deep learning prior