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1.发明公开公开(公告)号：US20230326596A1
公开(公告)日：2023-10-12
申请号：US17718898
申请日：2022-04-12
Applicant: CANON MEDICAL SYSTEMS CORPORATION
IPC: G16H50/20 , G06T7/00 , G06T11/00 , G06T7/11 , G06T7/62 , G16H30/40 , G06N3/08 , A61B6/12 , A61B6/03 , A61B6/00
CPC classification number: G16H50/20 , G06T7/0012 , G06T11/008 , G06T7/11 , G06T7/62 , G16H30/40 , G06N3/08 , A61B6/12 , A61B6/032 , A61B6/5258 , G06T2207/20081 , G06T2207/20084 , G06T2207/30052 , G06T2207/10081
Abstract: A method of processing information acquired by imaging performed by a medical image diagnostic apparatus, the method including but not limited to at least one of (A) acquiring a training image volume including at least one three-dimensional object having an embedded three-dimensional feature having a first cross-sectional area in a first three-dimensional plane; selecting a second cross-sectional area in a second three-dimensional plane containing the embedded three-dimensional feature, wherein the second cross-sectional area is larger than the first cross-sectional area; and training an untrained neural network with an image of the second cross-sectional area generated from the training image volume; and (B) acquiring a first set of training data; determining a first distribution of tissue density information from the first set of training data; generating from the first set of training data a second set of training data by performing at least one of a tissue-density shifting process and a tissue-density scaling process; and training an untrained neural network with the first and second sets of training data to obtain a trained neural network.
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2.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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3.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：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：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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Patent Agency Ranking
公开(公告)号：US20240260942A1
公开(公告)日：2024-08-08
申请号：US18165433
申请日：2023-02-07
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Ryosuke IWASAKI , Hiroki TAKAHASHI , Tomohisa IMAMURA , Ting XIA , Liang CAI , Jian ZHOU , Zhou YU
IPC: A61B8/08
CPC classification number: A61B8/5207 , A61B8/488 , G01S7/5206
Abstract: An ultrasound diagnosis apparatus according to an embodiment includes processing circuitry. This processing circuitry collects a first ultrasound signal including one or more harmonic components. By executing weighed addition processing where a coefficient distribution is applied to the first ultrasound signal for different directions of two or more dimensions, the processing circuitry generates a second ultrasound signal including components of each order at a ratio different, at a specific frequency, from a ratio among the frequency components included in the first ultrasound signal.
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公开(公告)号：US20240338800A1
公开(公告)日：2024-10-10
申请号：US18296840
申请日：2023-04-06
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Ting XIA , Jian ZHOU , Liang CAI , Zhou YU , Tomohisa IMAMURA , Ryosuke IWASAKI , Hiroki TAKAHASHI
CPC classification number: G06T5/70 , G06T7/0012 , G06T7/30 , G06T2207/10132 , G06T2207/20081 , G06T2207/20084 , G06T2207/20221 , G06T2207/30004
Abstract: An apparatus, method, and computer-readable medium having processing circuitry to receive first ultrasound data including at least one harmonic component, and apply the first ultrasound data to inputs of a trained deep neural network model that outputs enhanced ultrasound image data, the deep neural network model having been trained with training data including input ultrasound data and corresponding target ultrasound data having predetermined target features, and output the enhanced ultrasound image data.
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8.发明申请公开(公告)号：US20220367039A1
公开(公告)日：2022-11-17
申请号：US17730954
申请日：2022-04-27
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Liang CAI , Jian ZHOU , Ting XIA , Zhou YU , Tomohisa IMAMURA , Ryosuke IWASAKI , Hiroki TAKAHASHI
Abstract: A method and system enable to-be-processed medical image data and its corresponding noise characteristic information to be normalized to resemble noise characteristic information of training data used to train at least one neural network for at least one ultrasound data acquisition mode. After normalizing, this processed medical image data is input into the trained neural network for producing output data used for generating cleaner images. Noise characteristic information can be used directly in training a neural network, generating a trained neural network that can handle medical image data with various noise characteristics.
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公开(公告)号：US20220130520A1
公开(公告)日：2022-04-28
申请号：US17077413
申请日：2020-10-22
Inventor： Ting XIA , Zhou YU , Patrik ROGALLA , Bernice HOPPEL
Abstract: The present disclosure relates to a method for patient-specific optimization of imaging protocols. According to an embodiment, the present disclosure relates to a method for generating a patient-specific imaging protocol, comprising acquiring scout scan data, the scout scan data including scout scan information and scout scan parameters, generating a simulated image based on the acquired scout scan data, deriving a simulated dose map from the generated simulated image, determining image quality of the generated simulated image by applying machine learning to the generated simulated image, the neural network being trained to generate at least one probabilistic quality representation corresponding to at least one region of the generated simulated image, evaluating the determined image quality relative to a image quality threshold and the derived simulated dose map relative to a dosage threshold, optimizing. based on the evaluating, scan acquisition parameters and image reconstruction parameters, and generating, optimal imaging protocol parameters, wherein the optimal imaging protocol parameters maximize image quality while minimizing radiation exposure.
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公开(公告)号：US20210012543A1
公开(公告)日：2021-01-14
申请号：US16509408
申请日：2019-07-11
Applicant: Canon Medical Systems Corporation
Inventor： Ilmar HEIN , Zhou YU , Ting XIA
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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