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公开(公告)号：US11224399B2
公开(公告)日：2022-01-18
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：US11039806B2
公开(公告)日：2021-06-22
申请号：US16227251
申请日：2018-12-20
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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13.发明申请公开(公告)号：US20200234471A1
公开(公告)日：2020-07-23
申请号：US16252392
申请日：2019-01-18
Applicant: Canon Medical Systems Corporation
Inventor： Yujie Lu , Zhou Yu , Jian Zhou , Tzu-Cheng Lee , Richard Thompson
Abstract: A method and apparatus are provided for using a neural network to estimate scatter in X-ray projection images and then correct for the X-ray scatter. For example, the neural network is a three-dimensional convolutional neural network 3D-CNN to which are applied projection images, at a given view, for respective energy bins and/or material components. The projection images can be obtained by material decomposing spectral projection data, or by segmenting a reconstructed CT image into material-component images, which are then forward projected to generate energy-resolved material-component projections. The result generated by the 3D-CNN is an estimated scatter flux. To train the 3D-CNN, the target scatter flux in the training data can be simulated using a radiative transfer equation method.
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14.发明授权公开(公告)号：US10685461B1
公开(公告)日：2020-06-16
申请号：US16228512
申请日：2018-12-20
Inventor： Chung Chan , Zhou Yu , Jian Zhou , Patrik Rogalla , Bernice Hoppel , Kurt Walter Schultz
Abstract: A method and apparatus is provided to iteratively reconstruct a computed tomography (CT) image using a spatially-varying content-oriented regularization parameter, thereby achieving uniform statistical properties within respective organs/regions and different statistical properties (e.g., degree of smoothing and noise level) among the respective organs/regions. For example, less smoothing and sharper features/resolution can be applied within a lung region than within a soft-tissue region by using a smaller regularization parameter value in the lung region than in the soft-tissue region. This can be achieved, e.g., using a minimum intensity projection to suppress/eliminate sub-solid nodules in the lung region. The content-oriented regularization parameter can be generated by reconstructing an initial CT image, which is then segmented/classified according to organs and/or tissue type. Segmenting the image and generating the content-oriented regularization parameter can be integrated into one process by applying an HU-to-β mapping to the CT image.
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公开(公告)号：US12243127B2
公开(公告)日：2025-03-04
申请号：US17699008
申请日：2022-03-18
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Tzu-Cheng Lee , Jian Zhou , Liang Cai , Zhou Yu , Masakazu Matsuura , Takuya Nemoto , Hiroki Taguchi
Abstract: A medical image processing method includes obtaining a first set of projection data by performing, with a first CT apparatus including a first detector with a first pixel size, a first CT scan of an object in a first imaging region of the first detector; obtaining a first CT image with a first resolution by reconstructing the first set of projection data; obtaining a processed CT image with a resolution higher than the first resolution by applying a machine-learning model for resolution enhancement to the first CT image; and displaying the processed CT image or outputting the processed CT image for analysis. The machine-learning model is obtained by training using a second CT image based on a second set of projection data acquired by a second CT scan of the object in a second imaging region with a second CT apparatus including a second detector with a second pixel size.
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Patent Agency Ranking
公开(公告)号：US11712215B2
公开(公告)日：2023-08-01
申请号：US17229247
申请日：2021-04-13
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Qiulin Tang , Liang Cai , Zhou Yu , Jian Zhou
CPC classification number: A61B6/5264 , A61B6/032 , A61B6/5205 , G06T7/11 , G06T11/005 , G06T11/006 , G06T2211/436
Abstract: Devices, systems, and methods receive scan data that were generated by scanning a region of a subject with a computed tomography apparatus; generate multiple partial angle reconstruction (PAR) images based on the scan data; obtain corresponding characteristics of the multiple PAR images; perform correspondence mapping on the multiple PAR images based on the obtained corresponding characteristics and on the multiple PAR images, wherein the correspondence mapping generates correspondence-mapping data; and generate a motion-corrected reconstruction image based on the correspondence-mapping data and on one or both of the scan data and the PAR images.
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公开(公告)号：US20220327750A1
公开(公告)日：2022-10-13
申请号：US17699008
申请日：2022-03-18
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Tzu-Cheng Lee , Jian Zhou , Liang Cai , Zhou Yu , Masakazu Matsuura , Takuya Nemoto , Hiroki Taguchi
Abstract: A medical image processing method includes obtaining a first set of projection data by performing, with a first CT apparatus including a first detector with a first pixel size, a first CT scan of an object in a first imaging region of the first detector; obtaining a first CT image with a first resolution by reconstructing the first set of projection data; obtaining a processed CT image with a resolution higher than the first resolution by applying a machine-learning model for resolution enhancement to the first CT image; and displaying the processed CT image or outputting the processed CT image for analysis. The machine-learning model is obtained by training using a second CT image based on a second set of projection data acquired by a second CT scan of the object in a second imaging region with a second CT apparatus including a second detector with a second pixel size.
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18.发明申请公开(公告)号：US20220031274A1
公开(公告)日：2022-02-03
申请号：US16941760
申请日：2020-07-29
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Masakazu MATSUURA , Jian Zhou , Zhou Yu
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: on the basis of first subject data acquired by the imaging performed by the medical image diagnostic apparatus, acquiring noise data in the first subject data; on the basis of second subject data acquired by the imaging performed by a medical image diagnostic modality same kind as the medical image diagnostic apparatus and the noise data, acquiring synthesized subject data in which noises based on the noise data are added to the second subject data; and acquiring a noise reduction processing model by machine learning using the synthesized subject data and third subject data acquired by the imaging performed by the medical image diagnostic modality.
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公开(公告)号：US10945695B2
公开(公告)日：2021-03-16
申请号：US16231189
申请日：2018-12-21
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Abstract: A deep learning (DL) network reduces artifacts in computed tomography (CT) images based on complementary sparse-view projection data generated from a sparse kilo-voltage peak (kVp)-switching CT scan. The DL network is trained using input images exhibiting artifacts and target images exhibiting little to no artifacts. Another DL network can be trained to perform image-domain material decomposition of the artifact-mitigated images by being trained using target images in which beam hardening is corrected and spatial variations in the X-ray beam are accounted for. Further, material decomposition and artifact mitigation can be integrated in a single DL network that is trained using as inputs reconstructed images having artifacts and as targets material images without artifacts with beam-hardening corrections, etc. Further, the target material images can be transformed using a whitening transform to decorrelate noise.
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公开(公告)号：US20250005818A1
公开(公告)日：2025-01-02
申请号：US18343608
申请日：2023-06-28
Applicant: CANON MEDICAL SYSTEMS CORPORATION
IPC: G06T11/00
Abstract: According to some embodiments, a method comprises obtaining a group of reconstructed-image data; converting the group of reconstructed-image data to a derivative of Radon space, thereby generating Radon-space data, wherein the Radon-space data have a radial sampling pattern in the derivative of Radon space; and generating resampled data by inputting the Radon-space data into a first trained machine-learning model for resampling Radon-space data, wherein the resampled data have a cone-beam-projection-geometry-shaped sampling pattern in the derivative of Radon space.
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