[go: up one dir, main page]

Chen et al., 2013 - Google Patents

Improving abdomen tumor low-dose CT images using dictionary learning based patch processing and unsharp filtering

Chen et al., 2013

View HTML
Document ID
1238099627066666625
Author
Chen Y
Yu F
Luo L
Toumoulin C
Publication year
Publication venue
2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

External Links

Snippet

Reducing patient radiation dose, while maintaining a high-quality image, is a major challenge in Computed Tomography (CT). The purpose of this work is to improve abdomen tumor low-dose CT (LDCT) image quality by using a two-step strategy: a first patch-wise non …
Continue reading at pmc.ncbi.nlm.nih.gov (HTML) (other versions)

Classifications

    • 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
    • A61B6/02—Devices for diagnosis sequentially in different planes; Stereoscopic radiation diagnosis
    • A61B6/03—Computerised tomographs
    • A61B6/032—Transmission computed tomography [CT]
    • 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
    • G06T2207/10081—Computed x-ray tomography [CT]
    • 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]

Similar Documents

Publication Publication Date Title
Chen et al. Improving abdomen tumor low-dose CT images using a fast dictionary learning based processing
Kida et al. Visual enhancement of cone‐beam CT by use of CycleGAN
Wang et al. FBP-Net for direct reconstruction of dynamic PET images
Liu et al. 3D feature constrained reconstruction for low-dose CT imaging
Chen et al. Thoracic low-dose CT image processing using an artifact suppressed large-scale nonlocal means
Chen et al. Artifact suppressed dictionary learning for low-dose CT image processing
Zhang et al. Extracting information from previous full-dose CT scan for knowledge-based Bayesian reconstruction of current low-dose CT images
Mahnken et al. A new algorithm for metal artifact reduction in computed tomography: in vitro and in vivo evaluation after total hip replacement
Lee et al. Improved compressed sensing-based cone-beam CT reconstruction using adaptive prior image constraints
US20060285737A1 (en) Image-based artifact reduction in PET/CT imaging
Xue et al. LCPR-Net: low-count PET image reconstruction using the domain transform and cycle-consistent generative adversarial networks
Shangguan et al. Low-dose CT statistical iterative reconstruction via modified MRF regularization
Cui et al. Learning-based artifact removal via image decomposition for low-dose CT image processing
Feng et al. A preliminary study on projection denoising for low-dose CT imaging using modified dual-domain U-net
CN102024267A (en) Low-dose computed tomography (CT) image processing method based on wavelet space directional filtering
Chen et al. Improving abdomen tumor low-dose CT images using dictionary learning based patch processing and unsharp filtering
Schöndube et al. Evaluation of a novel CT image reconstruction algorithm with enhanced temporal resolution
Li et al. MARGANVAC: metal artifact reduction method based on generative adversarial network with variable constraints
Hu et al. DPI-MoCo: deep prior image constrained motion compensation reconstruction for 4D CBCT
Lee et al. Low-dose CBCT reconstruction via joint non-local total variation denoising and cubic B-spline interpolation
Sharma et al. Adversarial EM for variational deep learning: Application to semi-supervised image quality enhancement in low-dose PET and low-dose CT
CN1640361A (en) Positive computerized tomography restoration method for multi-phase horizontal set
Anam et al. A statistical-based automatic detection of a low-contrast object in the ACR CT phantom for measuring contrast-to-noise ratio of CT images
Chen et al. CT-Net: Cascaded T-shape network using spectral redundancy for dual-energy CT limited-angle reconstruction
Shi et al. Improving low-dose cardiac CT images based on 3D sparse representation