SELF CALIBRATION METHOD AND APPARATUS FOR CORRECTING OFFSET ANGLE IN A PHOTON COUNTING COMPUTED TOMOGRAPHY SYSTEM

    公开(公告)号:US20220296202A1

    公开(公告)日:2022-09-22

    申请号:US17206873

    申请日:2021-03-19

    Abstract: An apparatus, system and method for calibrating an x-ray apparatus including acquiring sinogram data by scanning a symmetrical phantom using a plurality of detector channels; generating mirror-copied sinogram data by mirror-copying at least one of first sinogram data and second sinogram data of the acquired sinogram data, wherein the first sinogram data and the second sinogram data are generated by dividing the sinogram data at a center detector channel of the plurality of detector channels; outputting a first reconstructed image by reconstructing the mirror-copied sinogram data; and determining a calibration parameter based on the first reconstructed image.

    APPARATUS AND METHOD COMBINING DEEP LEARNING (DL) WITH AN X-RAY COMPUTED TOMOGRAPHY (CT) SCANNER HAVING A MULTI-RESOLUTION DETECTOR

    公开(公告)号:US20210007694A1

    公开(公告)日:2021-01-14

    申请号:US16509369

    申请日:2019-07-11

    Abstract: A method and apparatus is provided that uses a deep learning (DL) network together with a multi-resolution detector to perform X-ray projection imaging to provide improved resolution similar to a single-resolution detector but at lower cost and less demand on the communication bandwidth between the rotating and stationary parts of an X-ray gantry. The DL network is trained using a training dataset that includes input data and target data. The input data includes projection data acquired using a multi-resolution detector, and the target data includes projection data acquired using a single-resolution, high-resolution detector. Thus, the DL network is trained to improve the resolution of projection data acquired using a multi-resolution detector. Further, the DL network is can be trained to additional correct other aspects of the projection data (e.g., noise and artifacts).

    PIXEL-BASED NUMBER OF ENERGY BIN MATERIAL DECOMPOSITION FOR REDUCING DATA REQUIREMENTS

    公开(公告)号:US20250127467A1

    公开(公告)日:2025-04-24

    申请号:US18490568

    申请日:2023-10-19

    Abstract: A photon-counting imaging system is provided. The system includes a photon-counting detector and processing circuitry. The detector acquires, from an imaging object, projection data for a plurality of projection views. The detector has a plurality of detector pixels that are arranged in both a channel direction and a segment direction on a surface of the detector. The processing circuitry obtains the projection data acquired by the detector. The projection data includes first and second projection data. The processing circuitry processes, with a first energy bin setting, the first projection data, the first energy bin setting having m energy bins, and processes, with a second energy bin setting, the second projection data, the second energy bin setting having n energy bins, where n>m. The processing circuitry generates, based on the processed first projection data and the processed second projection data, a material decomposition image of the imaging object.

    METHOD AND APPARATUS FOR PARTIAL VOLUME IDENTIFICATION FROM PHOTON-COUNTING MACRO-PIXEL MEASUREMENTS

    公开(公告)号:US20230083935A1

    公开(公告)日:2023-03-16

    申请号:US17469310

    申请日:2021-09-08

    Abstract: An apparatus and method to obtain input projection data based on radiation detected at a plurality of detector elements, reconstruct plural uncorrected images in response to applying a reconstruction algorithm to the input projection data, segment the plural uncorrected images into two or more types of material-component images by applying a deep learning segmentation network, generate output projection data corresponding to the two or more types of material-component images based on a forward projection, generate corrected multi material-decomposed projection data based on the generated output projection data corresponding to the two or more types of material-component images, and reconstruct the multi material-component images from the corrected multi material-decomposed projection data to generate one or more corrected images. In some embodiments, the plural uncorrected images are segmented into three or more types of material-component images by applying a deep learning segmentation network and beam hardening correction is performed for the three or more materials.

    APPARATUS AND METHOD FOR ARTIFACT DETECTION AND CORRECTION USING DEEP LEARNING

    公开(公告)号:US20210012543A1

    公开(公告)日:2021-01-14

    申请号:US16509408

    申请日:2019-07-11

    Abstract: A method and apparatus are provided that use deep learning (DL) networks to reduce noise and artifacts in reconstructed computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI) images. DL networks are used in both the sinogram and image domains. In each domain, a detection network is used to (i) determine if particular types of artifacts are exhibited (e.g., beam-hardening artifact, ring, motion, metal, photon-starvation, windmill, zebra, partial-volume, cupping, truncation, streak artifact, and/or shadowing artifacts), (ii) determine whether the detected artifact can be corrected through a changed scan protocol or image-processing techniques, and (iii) determine whether the detected artifacts are fatal, in which case the scan is stopped short of completion. When the artifacts can be corrected, corrective measures are taken through a changed scan protocol or through image processing to reduce the artifacts (e.g., convolutional neural network can be trained to perform the image processing).

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