3-D convolutional autoencoder for low-dose CT via transfer learning from a 2-D trained network

    公开(公告)号:US11580410B2

    公开(公告)日:2023-02-14

    申请号:US16964388

    申请日:2019-01-24

    Abstract: A 3-D convolutional autoencoder for low-dose CT via transfer learning from a 2-D trained network is described, A machine learning method for low dose computed tomography (LDCT) image correction is provided. The method includes training, by a training circuitry, a neural network (NN) based, at least in part, on two-dimensional (2-D) training data. The 2-D training data includes a plurality of 2-D training image pairs. Each 2-D image pair includes one training input image and one corresponding target output image. The training includes adjusting at least one of a plurality of 2-D weights based, at least in part, on an objective function. The method further includes refining, by the training circuitry, the NN based, at least in part, on three-dimensional (3-D) training data. The 3-D training data includes a plurality of 3-D training image pairs. Each 3-D training image pair includes a plurality of adjacent 2-D training input images and at least one corresponding target output image. The refining includes adjusting at least one of a plurality of 3-D weights based, at least in part, on the plurality of 2-D weights and based, at least in part, on the objective function. The plurality of 2-D weights includes the at least one adjusted 2-D weight.

    Energy-sensitive multi-contrast cost-effective CT system

    公开(公告)号:US11266363B2

    公开(公告)日:2022-03-08

    申请号:US15999466

    申请日:2017-02-17

    Abstract: Systems and methods for obtaining scattering images during computed tomography (CT) imaging are provided. Two gratings or grating layers can be disposed between the object to be imaged and the detector, and the gratings or grating layers can be arranged such that primary X-rays are blocked while scattered X-rays that are deflected as they pass through the object to be imaged reach the detector to generate the scattering image.

    FILTRATION METHODS FOR DUAL-ENERGY X-RAY CT

    公开(公告)号:US20210321961A1

    公开(公告)日:2021-10-21

    申请号:US17354286

    申请日:2021-06-22

    Abstract: Systems and method for performing X-ray computed tomography (CT) that can improve spectral separation and decrease motion artifacts without increasing radiation dose are provided. The systems and method can be used with either a kVp-switching source or a single-kVp source. When used with a kVp-switching source, an absorption grating and a filter grating can be disposed between the X-ray source and the sample to be imaged. Relative motion of the filter and absorption gratings can by synchronized to the kVp switching frequency of the X-ray source. When used with a single-kVp source, a combination of absorption and filter gratings can be used and can be driven in an oscillation movement that is optimized for a single-kVp X-ray source. With a single-kVp source, the absorption grating can also be omitted and the filter grating can remain stationary.

    A SYNERGIZED PULSING-IMAGING NETWORK (SPIN)

    公开(公告)号:US20210149005A1

    公开(公告)日:2021-05-20

    申请号:US16768834

    申请日:2018-12-07

    Inventor: Ge Wang Qing Lyu Tao Xu

    Abstract: A synergized pulsing-imaging network is described. A method of optimizing a magnetic resonance imaging (MRI) system includes optimizing, by a synergized pulsing-imaging network (SPIN) circuitry a pulse sequence based, at least in part, on a loss function associated with a reconstruction network. The method further includes optimizing, by the SPIN circuitry, the reconstruction network based, at least in part, on intermediate raw MRI data and based, at least in part, on a ground truth MRI image data. The intermediate raw MRI data is determined based, at least in part on the pulse sequence.

    Apparatus and method for K-edge based interior tomography image processing

    公开(公告)号:US11006916B2

    公开(公告)日:2021-05-18

    申请号:US15452802

    申请日:2017-03-08

    Abstract: Provided are an apparatus and method for K-edge based interior tomography image processing. The apparatus efficiently performs tomography image reconstruction by acquiring different material images using an energy-selective detector, such as a photon counting detector. The apparatus includes a projection image acquisition unit configured to inject a contrast agent into an object and acquire projection images using the energy-selective detector with respect to a K-edge of the material, a parameter initialization unit configured to initialize parameters needed for image reconfiguration, an ROI interior reconstruction unit configured to perform reconstruction for image reconfiguration until initial convergence is achieved in order to reconstruct an interior of a region of interest, a concentration and correction value calculation unit configured to calculate a concentration and a correction value (β) using a linear attenuation coefficient of the contrast agent and a linear attenuation coefficient of blood, which are known, in the acquired and reconstructed image.

    SYSTEMS AND METHODS FOR INTEGRATING TOMOGRAPHIC IMAGE RECONSTRUCTION AND RADIOMICS USING NEURAL NETWORKS

    公开(公告)号:US20200380673A1

    公开(公告)日:2020-12-03

    申请号:US16621800

    申请日:2018-06-18

    Abstract: Computed tomography (CT) screening, diagnosis, or another image analysis tasks are performed using one or more networks and/or algorithms to either integrate complementary tomographic image reconstructions and radiomics or map tomographic raw data directly to diagnostic findings in the machine learning framework. One or more reconstruction networks are trained to reconstruct tomographic images from a training set of CT projection data. One or more radiomics networks are trained to extract features from the tomographic images and associated training diagnostic data. The networks/algorithms are integrated into an end-to-end network and trained. A set of tomographic data, e.g., CT projection data, and other relevant information from an individual is input to the end-to-end network, and a potential diagnosis for the individual based on the features extracted by the end-to-end network is produced. The systems and methods can be applied to CT projection data, MRI data, nuclear imaging data, ultrasound signals, optical data, other types of tomographic data, or combinations thereof.

    MONOCHROMATIC CT IMAGE RECONSTRUCTION FROM CURRENT-INTEGRATING DATA VIA MACHINE LEARNING

    公开(公告)号:US20200273215A1

    公开(公告)日:2020-08-27

    申请号:US16647220

    申请日:2018-09-26

    Abstract: A machine-learning-based monochromatic CT image reconstruction method is described for quantitative CT imaging. The neural network is configured to learn a nonlinear mapping function from a training data set to map a CT image, which is reconstructed from a single spectral current-integrating projection data set, to monochromatic projections at a pre-specified energy level, realizing monochromatic CT imaging and overcoming beam hardening. An apparatus, method and/or system are configured to determine, by a trained artificial neural network (ANN), a monochromatic projection data set based, at least in part, on a measured CT image. The measured CT image may be reconstructed based, at least in part, on measured projection data. The measured projection data may be polychromatic. The apparatus, method and/or system may be further configured to reconstruct a monochromatic CT image based, at least in part, on the monochromatic projection data set.

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