[go: up one dir, main page]

Li et al., 2018 - Google Patents

Multienergy cone-beam computed tomography reconstruction with a spatial spectral nonlocal means algorithm

Li et al., 2018

View PDF
Document ID
9996380604212876330
Author
Li B
Shen C
Chi Y
Yang M
Lou Y
Zhou L
Jia X
Publication year
Publication venue
SIAM journal on imaging sciences

External Links

Snippet

Multienergy computed tomography (CT) is an emerging medical image modality with a number of potential applications in diagnosis and therapy. However, high system cost and technical barriers obstruct its step into routine clinical practice. In this study, we propose a …
Continue reading at pmc.ncbi.nlm.nih.gov (PDF) (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
    • G06T2207/10084Hybrid tomography; Concurrent acquisition with multiple different tomographic modalities
    • 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
    • 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
    • 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
    • G06T2207/10Image acquisition modality
    • G06T2207/10116X-ray image
    • 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
    • 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
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/20Special algorithmic details
    • 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]
    • 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
    • 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

Similar Documents

Publication Publication Date Title
US12502077B2 (en) Method for reducing noise in a medical image slice by inputting plural slice images into a trained neural network
Nomura et al. Projection‐domain scatter correction for cone beam computed tomography using a residual convolutional neural network
Dong et al. Deep learning-based attenuation correction in the absence of structural information for whole-body positron emission tomography imaging
EP3735177B1 (en) Full dose pet image estimation from low-dose pet imaging using deep learning
Lu et al. An investigation of quantitative accuracy for deep learning based denoising in oncological PET
JP7433883B2 (en) Medical equipment and programs
CN109805950B (en) Medical image processing device and medical image processing system
US9159122B2 (en) Image domain de-noising
US9836824B2 (en) De-noised reconstructed image data edge improvement
US9600924B2 (en) Iterative reconstruction of image data in CT
Jia et al. GPU-based fast low-dose cone beam CT reconstruction via total variation
Li et al. Multienergy cone-beam computed tomography reconstruction with a spatial spectral nonlocal means algorithm
He et al. Downsampled imaging geometric modeling for accurate CT reconstruction via deep learning
Xue et al. A 3D attention residual encoder–decoder least-square GAN for low-count PET denoising
Zhang et al. PET image reconstruction using a cascading back-projection neural network
Liang et al. Guest editorial low-dose CT: what has been done, and what challenges remain?
Xu et al. Statistical iterative reconstruction to improve image quality for digital breast tomosynthesis
Jiang et al. A feasibility study of enhanced prompt gamma imaging for range verification in proton therapy using deep learning
Shi et al. Learned tensor neural network texture prior for photon-counting CT reconstruction
Ghafari et al. Generation of 18F-FDG PET standard scan images from short scans using cycle-consistent generative adversarial network
Park et al. Implicit neural representation‐based method for metal‐induced beam hardening artifact reduction in X‐ray CT imaging
Peng et al. Dual-energy cone-beam CT using two complementary limited-angle scans with a projection-consistent diffusion model
Li et al. Effective noise‐suppressed and artifact‐reduced reconstruction of SPECT data using a preconditioned alternating projection algorithm
US20250131615A1 (en) Systems and methods for accelerating spect imaging
Li et al. Low-dose sinogram restoration enabled by conditional GAN with cross-domain regularization in SPECT imaging