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.

公开(公告)号：US20240008832A1

公开(公告)日：2024-01-11

申请号：US18371486

申请日：2023-09-22

Applicant: CANON MEDICAL SYSTEMS CORPORATION

Inventor： Chung CHAN ,  Jian ZHOU ,  Evren ASMA

IPC: A61B6/00 ,  A61B6/03 ,  G06N3/08 ,  G06T7/00

CPC classification number: A61B6/5258 ,  A61B6/032 ,  A61B6/037 ,  G06N3/08 ,  G06T7/0012 ,  G06T2207/10004 ,  G06T2207/20081 ,  G06T2207/20084

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.

公开(公告)号：US20230380788A1

公开(公告)日：2023-11-30

申请号：US17825650

申请日：2022-05-26

Applicant: CANON MEDICAL SYSTEMS CORPORATION

Inventor： Tzu-Cheng LEE ,  Jian ZHOU ,  Liang CAI ,  Zhou YU

IPC: A61B6/00 ,  A61B6/03

CPC classification number: A61B6/5205 ,  A61B6/032 ,  G06T11/008

Abstract: An information processing method processes an x-ray image including the steps of: obtaining first lower-radiation dose three-dimensional image data during a first scan of a patient; and detecting, using a trained neural network, a presence of an artifact (e.g., a metal artifact) in the first lower-radiation dose three-dimensional image data. An information processing apparatus includes processing circuitry for performing the detection method, and computer instructions stored in a non-transitory computer readable storage medium cause a computer processor to performing the detection method.

公开(公告)号：US20230145523A1

公开(公告)日：2023-05-11

申请号：US17680048

申请日：2022-02-24

Applicant: CANON MEDICAL SYSTEMS CORPORATION

Inventor： Masakazu MATSUURA ,  Jian ZHOU ,  Zhou YU

IPC: G06T11/00 ,  G06T7/30 ,  A61B6/00 ,  A61B6/03

CPC classification number: G06T11/008 ,  G06T7/30 ,  A61B6/5258 ,  A61B6/032 ,  G06T2207/10081 ,  G06T2207/20081 ,  G06T2207/10116 ,  A61B6/563

Abstract: A medical image processing apparatus according to an embodiment includes processing circuitry configured to acquire a radiographic image of a subject and acquire a post-processing image with reduced steak artifacts by applying a model that reduces streak artifacts to the radiographic image, wherein the model is generated by machine learning that uses, as a training data pair, a first radiographic image and a second radiographic image based on artifact generation processing for generating streak artifacts.

公开(公告)号：US20220104787A1

公开(公告)日：2022-04-07

申请号：US17554032

申请日：2021-12-17

Applicant: Canon Medical Systems Corporation

Inventor： Chung CHAN ,  Jian ZHOU ,  Evren ASMA

IPC: A61B6/00 ,  A61B6/03 ,  G06N3/08 ,  G06T7/00

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 produceuniform 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.

公开(公告)号：US20240412426A1

公开(公告)日：2024-12-12

申请号：US18332237

申请日：2023-06-09

Applicant: CANON MEDICAL SYSTEMS CORPORATION

Inventor： Yujie LU ,  Liang CAI ,  Jian ZHOU

IPC: G06T11/00 ,  G01N23/046 ,  G06T3/40 ,  G06T7/00

Abstract: A method for scatter estimation in a CT including a detector having multiple detector pixels includes: obtaining projection data by scanning an imaging object; reconstructing image data from the projection data; estimating, based on the projection data, a first scatter distribution; selecting, based on the first scatter distribution, a first subset of the pixels; calculating, based on the projection data and the image data, a second scatter distribution with respect to the selected first subset, the second scatter distribution having higher accuracy than the first scatter distribution; acquiring, based on the second scatter distribution, a third scatter distribution with respect to a second subset of the pixels, the third scatter distribution having higher spatial resolution than the second scatter distribution.

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

IPC: G06T5/00 ,  G06T7/00 ,  G06T7/30

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.

公开(公告)号：US20240062371A1

公开(公告)日：2024-02-22

申请号：US18448773

申请日：2023-08-11

Applicant: CANON MEDICAL SYSTEMS CORPORATION

Inventor： Chih-Chieh LIU ,  Jian ZHOU ,  Qiulin TANG ,  Liang CAI ,  Zhou YU

IPC: G06T7/00 ,  G06T7/11

CPC classification number: G06T7/0012 ,  G06T7/11 ,  G06T2207/20084 ,  G06T2207/30048 ,  G06T2207/30101 ,  G06T2200/04

Abstract: An apparatus is provided with processing circuitry that receives a phase image acquired at a corresponding cardiac phase, determines, from the received phase image, a mask image of a particular cardiac region, applies both the determined mask image and the phase image to inputs of a trained neural network model to obtain, from outputs of the neural network model, a location probability map. The neural network model is trained with a set of input data and a corresponding set of output data. The input data includes a training mask image and a training phase image, and the output data includes a training location probability map. The processing circuitry calculates, for the cardiac phase, from the determined location probability map output from the trained neural network model, a value of a cardiac motion metric. The determined location probability map specifies a probable location of a cardiac vessel.

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

IPC: G16H30/40 ,  A61B8/08 ,  G06T5/00 ,  G06N3/08

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.

公开(公告)号：US20220327662A1

公开(公告)日：2022-10-13

申请号：US17705030

申请日：2022-03-25

Applicant: CANON MEDICAL SYSTEMS CORPORATION

Inventor： Masakazu MATSUURA ,  Takuya NEMOTO ,  Hiroki TAGUCHI ,  Tzu-cheng LEE ,  Jian ZHOU ,  Liang CAI ,  Zhou YU

IPC: G06T3/40 ,  G06T5/00 ,  G06T5/50

Abstract: A medical data processing method according to an embodiment includes inputting first medical data relating to a subject imaged with a medical image capture apparatus to a learned model to configured to generate second medical data having lower noise than that of the first medical data and having a super resolution compared with the first medical data based on the first medical data to output the second medical data.