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11.发明公开公开(公告)号：US20230284997A1
公开(公告)日：2023-09-14
申请号：US17692697
申请日：2022-03-11
Applicant: CANON MEDICAL SYSTEMS CORPORATION
CPC classification number: A61B6/544 , G06T11/008 , A61B6/5258 , G06T2210/41 , G06T2200/04 , A61B6/025
Abstract: A method, apparatus, and computer-readable storage medium for controlling exposure/irradiation during a main three-dimensional X-ray imaging scan using at least one spatially-distributed characteristic of a pre-scan/scout scan preceding the main scan. The at least one spatially-distributed characteristic includes (1) a spatially-distributed noise characteristic of the pre-scan and/or (2) a spatially-distributed identification of exposure-sensitive tissue types. The at least one spatially-distributed characteristic can be calculated from images reconstructed from sinogram/projection data and/or from sinogram/projection directly using a neural network.
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12.发明申请公开(公告)号：US20220139006A1
公开(公告)日：2022-05-05
申请号：US17577689
申请日：2022-01-18
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Abstract: An information processing method of an embodiment is a processing method of information acquired by imaging performed by a medical image diagnostic apparatus, the information processing method includes the steps of: acquiring noise data by imaging a phantom using a medical imaging apparatus; based on first subject projection data acquired by the imaging performed by a medical image diagnostic modality of a same kind as the medical image diagnostic apparatus and the noise data, acquiring synthesized subject data in which noise based on the noise data is added to the first subject projection data; and acquiring a noise reduction processing model by machine learning using the synthesized subject data and second subject projection data acquired by the imaging performed by the medical image diagnostic modality.
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公开(公告)号：US20210335023A1
公开(公告)日：2021-10-28
申请号：US16860425
申请日：2020-04-28
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Wenyuan QI , Yujie LU , Evren ASMA , Yi QIANG , Jeffrey KOLTHAMMER , Zhou YU
Abstract: The present disclosure relates to an apparatus for estimating scatter in positron emission tomography, comprising processing circuitry configured to acquire an emission map and an attenuation map, each representing an initial image reconstruction of a positron emission tomography scan, calculate, using a radiative transfer equation (RTE) method, a scatter source map of a subject of the positron emission tomography scan based on the emission map and the attenuation map, estimate, using the RTE method and based on the emission map, the attenuation map, and the scatter source map, scatter, and perform an iterative image reconstruction of the positron emission tomography scan based on the estimated scatter and raw data from the positron emission tomography scan of the subject.
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公开(公告)号：US20210290193A1
公开(公告)日：2021-09-23
申请号：US17339093
申请日：2021-06-04
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Jian ZHOU , Ruoqiao ZHANG , Zhou YU , Yan LIU
Abstract: A deep learning (DL) network corrects/performs sinogram completion in computed tomography (CT) images based on complementary high- and low-kV projection data generated from a sparse (or fast) kilo-voltage (kV)-switching CT scan. The DL network is trained using inputs and targets, which respectively generated with and without kV switching. Another DL network can be trained to correct sinogram-completion errors in the projection data after a basis/material decomposition. A third DL network can be trained to correct sinogram-completion errors in reconstructed images based on the kV-switching projection data. Performance of the DL network can be improved by dividing a 3D convolutional neural network (CNN) into two steps performed by respective 2D CNNs. Further, the projection data and DLL can be divided into high- and low-frequency components to improve performance.
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公开(公告)号：US20210007702A1
公开(公告)日：2021-01-14
申请号：US16510594
申请日：2019-07-12
Applicant: Canon Medical Systems Corporation
Inventor： Tzu-Cheng LEE , Jian ZHOU , Zhou YU
Abstract: A method and apparatus is provided that uses a deep learning (DL) network to correct projection images acquired using an X-ray source with a large focal spot size. The DL network is trained using a training dataset that includes input data and target data. The input data includes large-focal-spot-size X-ray projection data, and the output data includes small-focal-spot-size X-ray projection data (i.e., smaller than the focal spot of the input data). Thus, the DL network is trained to improve the resolution of projection data acquired using a large focal spot size, and obtain a resolution similar to what is achieved using a small focal spot size. Further, the DL network is can be trained to additional correct other aspects of the projection data (e.g., denoising the projection data).
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Patent Agency Ranking
公开(公告)号：US20240257361A1
公开(公告)日：2024-08-01
申请号：US18424310
申请日：2024-01-26
Applicant: iTOMOGRAPHY CORPORATION , THE UNIVERSITY OF CENTRAL FLORIDA RESEARCH FOUNDATION, Inc. , CANON MEDICAL SYSTEMS CORPORATION
Inventor： Seongjin YOON , Alexander KATSEVICH , Michael FRENKEL , Qiulin TANG , Liang CAl , Jian ZHOU , Zhou YU
CPC classification number: G06T7/248 , G06T7/62 , G06T11/008 , G06T2207/10081 , G06T2207/20021 , G06T2207/30048 , G06T2207/30101 , G06T2210/41
Abstract: A method for motion estimation in CT systems is provided. The method includes dividing projection data, obtained by scanning a heart using the CT system, into a plurality of partial-angle-reconstruction (PAR) bins, reconstructing a plurality of PAR volumes from the PAR-binned projection data, obtaining, based on the plurality of reconstructed PAR volumes, a number of short-scan volumes, determining, based on the obtained number of short-scan volumes, a plurality of nodes throughout the heart, estimating, for each of the determined plurality of nodes, a plurality of model parameters of a motion model, and generating, based on the plurality of model parameters estimated for each of the plurality of nodes, parameters of a global motion model at each voxel of a volume of the heart. The method also includes reconstructing, based on the generated motion parameters of the global motion model at each voxel of the volume of the heart, a motion-compensated short-scan volume.
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公开(公告)号：US20230119427A1
公开(公告)日：2023-04-20
申请号：US17985236
申请日：2022-11-11
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Abstract: A method and apparatus is provided that uses a deep learning (DL) network to reduce noise and artifacts in reconstructed medical images, such as images generated using computed tomography, positron emission tomography, and magnetic resonance imaging. The DL network can operate either on pre-reconstruction data or on a reconstructed image. The DL network can be an artificial neural network or a convolutional neural network (e.g., using a three-channel volumetric kernel architecture). Different neural networks can be trained depending on the noise level, scanning protocol, or the anatomic, diagnostic or clinical objective of the reconstructed image (e.g., by partitioning the training data into noise-level range and training respective DL networks for each range). Further, the DL networks can be trained to mitigate artifacts, such as the cone-beam artifact.
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18.发明申请公开(公告)号：US20230083935A1
公开(公告)日：2023-03-16
申请号：US17469310
申请日：2021-09-08
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Yujie LU , Ilmar HEIN , Zhou YU
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.
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公开(公告)号：US20230067596A1
公开(公告)日：2023-03-02
申请号：US17462391
申请日：2021-08-31
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Qiulin TANG , Ruoqiao ZHANG , Jian ZHOU , Zhou YU
Abstract: Data acquired from a scan of an object can be decomposed into frequency components. The frequency components can be input into a trained model to obtain processed frequency components. These processed frequency components can be composed and used to generate a final image. The trained model can be trained, independently or dependently, using frequency components covering the same frequencies as the to-be-processed frequency components. In addition, organ specific processing can be enabled by training the trained model using image and/or projection datasets of the specific organ.
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公开(公告)号：US20220399101A1
公开(公告)日：2022-12-15
申请号：US17343519
申请日：2021-06-09
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Abstract: The present disclosure relates to a spatially-variant model of a point spread function and its role in enhancing medical image resolution. For instance, a method of the present disclosure comprises receiving a first medical image having a first resolution, applying a neural network to the first medical image, the neural network including a first subset of layers and, subsequently, a second subset of layers, the first subset of layers of the neural network generating, from the first medical image, a second medical image having a second resolution and the second subset of layers of the neural network generating, from the second medical image, a third medical image having a third resolution, and outputting the third medical image, wherein the first resolution is lower than the second resolution and the second resolution is lower than the third resolution.
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