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.

    METHODS AND SYSTEMS FOR STATIONARY COMPUTED TOMOGRAPHY

    公开(公告)号:US20190261930A1

    公开(公告)日:2019-08-29

    申请号:US16347828

    申请日:2017-11-07

    Abstract: Methods and systems for stationary computed tomography are disclosed. The methods and systems include a gantry having alternating x-ray sources and x-ray detectors that are stationary during operation of the system. The gantry and pairs of x-ray sources and detectors substantially surrounds an object positioned inside the gantry during operation of the system. Dynamically adjustable collimators are positioned between the x-ray sources and the object. Each of the x-ray sources projects an x-ray beam through the collimators and through the object and the x-ray detectors receive the x-ray beam. The x-ray detectors include means for converting the x-ray beam to raw image data. One or more microprocessors control the x-ray sources and the process raw image data. A data storage device stores instructions, which upon execution by the microprocessor, control the x-ray sources and process the raw image data by converting the raw image data to a digital image.

    Image reconstruction method for computed tomography

    公开(公告)号:US12154193B2

    公开(公告)日:2024-11-26

    申请号:US17859061

    申请日:2022-07-07

    Abstract: Systems and methods for reconstructing images for computed tomography are provided. Image reconstruction can be based on a realistic polychromatic physical model, and can include use of both an analytical algorithm and a single-variable optimization method. The optimization method can be used to solve the non-linear polychromatic X-ray integral model in the projection domain, resulting in an accurate decomposition for sinograms of two physical basis components.

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