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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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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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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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Patent Agency Ranking
公开(公告)号：US20220110600A1
公开(公告)日：2022-04-14
申请号：US17554019
申请日：2021-12-17
Applicant: Canon Medical Systems Corporation
Inventor： Chung CHAN , Jian ZHOU , Evren ASMA
Abstract: A deep learning (DL) convolution neural network (CNN) reduces noise in positron emission tomography (PET) images, and is trained using a range of noise levels for the low-quality images having high noise in the training dataset to produce uniform high-quality images having low noise, independently of the noise level of the input image. The DL-CNN network can be implemented by slicing a three-dimensional (3D) PET image into 2D slices along transaxial, coronal, and sagittal planes, using three separate 2D CNN networks for each respective plane, and averaging the outputs from these three separate 2D CNN networks. Feature-oriented training can be implemented by segmenting each training image into lesion and background regions, and, in the loss function, applying greater weights to voxels in the lesion region. Other medical images (e.g. MRI and CT) can be used to enhance resolution of the PET images and provide partial volume corrections.
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公开(公告)号：US20250166247A1
公开(公告)日：2025-05-22
申请号：US18516450
申请日：2023-11-21
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Qiulin TANG , Jian ZHOU , Liang CAI , Chih-chieh LIU , Zhou YU
Abstract: A method for performing cardiac motion compensation in a computed tomography (CT) imaging system is provided. The method includes receiving projection data acquired from an imaging object by the CT imaging system. The method also includes, until a predefined termination criterion is met, iteratively reconstructing, based on estimated cardiac motion, the received projection data to generate a motion-compensated image of the imaging object, determining a vessel region of interest (ROI) within the generated motion-compensated image, and updating the estimated cardiac motion, based on an optimization cost function associated with the determined vessel ROI. The method further includes outputting, as a final reconstructed image of the imaging object, the generated motion-compensated image.
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公开(公告)号：US20240398386A1
公开(公告)日：2024-12-05
申请号：US18329070
申请日：2023-06-05
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Ryosuke IWASAKI , Hiroki TAKAHASHI , Tomohisa IMAMURA , Ting XIA , Liang CAI , Jian ZHOU
Abstract: An ultrasound diagnosis apparatus of an embodiment includes storage circuitry and processing circuitry. The storage circuitry stores therein a trained model trained using a first ultrasound signal containing a saturated signal as input data and a second ultrasound signal in which effect of saturation is reduced from the first ultrasound signal, as target data. The processing circuitry inputs a third ultrasound signal containing a saturated signal to the trained model and acquires a fourth ultrasound signal that is output from the trained model and in which effect of saturation is reduced from the third ultrasound signal, to generate the fourth ultrasound signal.
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公开(公告)号：US20240389961A1
公开(公告)日：2024-11-28
申请号：US18323016
申请日：2023-05-24
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Tzu-Cheng LEE , Liang CAI , Jian ZHOU
Abstract: A method of controlling computed tomography (CT) scanning includes performing a scout CT scan of a 3D region of a head of a subject to be examined, using a CT gantry having an X-ray source and an X-ray detector both rotatably supported thereby, to produce image data. Anatomical landmarks are detected for identifying an orbitomeatal base line (OMBL), by inputting cross-sectional image data of the 3D region generated from the image data to a trained machine learning model. A tilt angle of the CT gantry is determined based on the detected anatomical landmarks. A diagnostic CT scan of the object is performed using the CT gantry tilted at the determined tilt angle.
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公开(公告)号：US20240378496A1
公开(公告)日：2024-11-14
申请号：US18361238
申请日：2023-07-28
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Ting XIA , Jian ZHOU , Liang CAI , Zhou YU , Tomohisa IMAMURA , Ryosuke IWASAKI , Hiroki TAKAHASHI
IPC: G06N20/00
Abstract: A method for harmonic imaging is provided and includes inputting first training ultrasound data, including a fundamental component and a harmonic component, to each of a plurality of teacher models, and training each teacher model with the first training ultrasound as teacher input data and second training ultrasound data, including the harmonic component, as teacher target data; acquiring, for each teacher, corresponding first estimated data output from the teacher model, in response to input of first ultrasound data to the teacher model; selecting a first particular teacher model by evaluating the corresponding first estimated data output from each of the trained teacher models; and training a student model with the first ultrasound data as student input data and the corresponding first estimated data of the selected first particular teacher model as student target data.
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