公开(公告)号：US12299841B2

公开(公告)日：2025-05-13

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: G06T3/4053 ,  G06T3/4046 ,  G06T5/50 ,  G06T5/70

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.

公开(公告)号：US12205199B2

公开(公告)日：2025-01-21

申请号：US17577689

申请日：2022-01-18

Applicant: CANON MEDICAL SYSTEMS CORPORATION

Inventor： Masakazu Matsuura ,  Yujie Lu ,  Jian Zhou ,  Zhou Yu ,  Liang Cai ,  Ting Xia

IPC: G06T11/00 ,  A61B6/00 ,  A61B6/03 ,  A61B6/58

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.

公开(公告)号：US12064281B2

公开(公告)日：2024-08-20

申请号：US16938463

申请日：2020-07-24

Applicant: The Regents of the University of California ,  CANON MEDICAL SYSTEMS CORPORATION

Inventor： Jinyi Qi ,  Nimu Yuan ,  Jian Zhou

IPC: G06T5/00 ,  A61B6/00 ,  G06N3/08

CPC classification number: A61B6/5211 ,  A61B6/5258 ,  A61B6/542 ,  G06N3/08 ,  G06T5/002 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: First and second substantially independent identically distributed half scans are obtained; the first substantially independent identically distributed half scan is used as training data to train a machine learning-based system, and the second substantially independent identically distributed half scan is used as label data to train a machine learning-based system. This produces a trained machine learning-based system.

公开(公告)号：US12062153B2

公开(公告)日：2024-08-13

申请号：US17369596

申请日：2021-07-07

Applicant: CANON MEDICAL SYSTEMS CORPORATION

Inventor： Yujie Lu ,  Qiulin Tang ,  Zhou Yu ,  Jian Zhou

IPC: G06T5/20 ,  G06N3/08 ,  G06T5/50 ,  G16H30/40

CPC classification number: G06T5/20 ,  G06N3/08 ,  G06T5/50 ,  G16H30/40 ,  G06T2200/04 ,  G06T2207/10116 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30004

Abstract: An apparatus, method, and computer-readable medium for improving image quality of a medical volume. In an embodiment, the apparatus includes processing circuitry configured to receive a reconstructed input image volume from X-ray projection data corresponding to a three-dimensional region of an object to be examined, apply a pseudo-three-dimensional neural network (P3DNN) to the reconstructed input image volume, the application of the pseudo-three-dimensional neural network including generating, for the reconstructed input image volume, a plurality of three-dimensional image datasets representing a different anatomical plane of the reconstructed input image volume, applying at least one convolutional filter to each of a sagittal plane dataset, a transverse plane dataset, and a coronal plane dataset, and concatenating results of the applied at least one convolutional filter to generate an intermediate output image volume, and generate, based on the application of the P3DNN, an output image volume corresponding to the three-dimensional region of the object.

公开(公告)号：US12016718B2

公开(公告)日：2024-06-25

申请号：US17692697

申请日：2022-03-11

Applicant: CANON MEDICAL SYSTEMS CORPORATION

Inventor： Ting Xia ,  Liang Cai ,  Jian Zhou ,  Zhou Yu

IPC: A61B6/00 ,  A61B6/02 ,  A61B6/03 ,  G06T11/00

CPC classification number: A61B6/544 ,  A61B6/5258 ,  G06T11/008 ,  A61B6/025 ,  A61B6/032 ,  G06T2200/04 ,  G06T2210/41

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.

公开(公告)号：US11984218B2

公开(公告)日：2024-05-14

申请号：US17343519

申请日：2021-06-09

Applicant: CANON MEDICAL SYSTEMS CORPORATION

Inventor： Liang Cai ,  Jian Zhou ,  Zhou Yu

IPC: G06K9/00 ,  G06T3/4046 ,  G06T5/50 ,  G16H30/40

CPC classification number: G16H30/40 ,  G06T3/4046 ,  G06T5/50 ,  G06T2207/10081 ,  G06T2207/20016

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.

公开(公告)号：US11961209B2

公开(公告)日：2024-04-16

申请号：US17013104

申请日：2020-09-04

Applicant: CANON MEDICAL SYSTEMS CORPORATION

Inventor： Chung Chan ,  Jian Zhou ,  Evren Asma

IPC: G06T5/00 ,  G06N3/08

CPC classification number: G06T5/002 ,  G06N3/08 ,  G06T2207/10104 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: A system and method for training a neural network to denoise images. One noise realization is paired to an ensemble of training-ready noise realizations, and fed into a neural network for training. Training datasets can also be retrospectively generated based on existing patient studies to increase the number of training datasets.

公开(公告)号：US11847761B2

公开(公告)日：2023-12-19

申请号：US17010313

申请日：2020-09-02

Applicant: CANON MEDICAL SYSTEMS CORPORATION

Inventor： Jian Zhou ,  Zhou Yu ,  Yan Liu

IPC: G06T5/00 ,  G06T7/00 ,  G16H30/20 ,  G06T5/50 ,  G06T11/00 ,  G06N3/08 ,  G06V30/244 ,  G06F18/10

CPC classification number: G06T5/002 ,  G06F18/10 ,  G06N3/08 ,  G06T5/50 ,  G06T7/0014 ,  G06T11/003 ,  G06T11/008 ,  G06V30/244 ,  G16H30/20 ,  G06T2207/10081 ,  G06T2207/10088 ,  G06T2207/10104 ,  G06T2207/10108 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30004

Abstract: A medical image processing apparatus according to an embodiment comprises a memory and processing circuitry. The memory is configured to store a plurality of neural networks corresponding to a plurality of imaging target sites, respectively, the neural networks each including an input layer, an output layer, and an intermediate layer between the input layer and the output layer, and each generated through learning processing with multiple data sets acquired for the corresponding imaging target site. The processing circuitry is configured to process first data into second data using, among the neural networks, the neural network corresponding to the imaging target site for the first data, wherein the first data is input to the input layer and the second data is output from the output layer.

公开(公告)号：US11672498B2

公开(公告)日：2023-06-13

申请号：US16941760

申请日：2020-07-29

Applicant: CANON MEDICAL SYSTEMS CORPORATION

Inventor： Masakazu Matsuura ,  Jian Zhou ,  Zhou Yu

IPC: G06T5/00 ,  A61B6/00 ,  G06N3/08 ,  A61B6/03

CPC classification number: A61B6/5211 ,  A61B6/032 ,  A61B6/5258 ,  G06N3/08 ,  G06T5/002 ,  G06T2207/20081

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.

公开(公告)号：US11331064B2

公开(公告)日：2022-05-17

申请号：US16915722

申请日：2020-06-29

Applicant: CANON MEDICAL SYSTEMS CORPORATION

Inventor： Jian Zhou ,  Zhou Yu ,  Yan Liu

IPC: A61B6/00 ,  A61B6/04 ,  A61B6/02

Abstract: An apparatus and method are described using a forward model to correct pulse pileup in spectrally resolved X-ray projection data from photon-counting detectors (PCDs). To calibrate the forward model, which represents each order of pileup using a respective pileup response matrix (PRM), an optimization search determines the elements of the PRMs that optimize an objective function measuring agreement between the spectra of recorded counts affected by pulse pileup and the estimated counts generated using forward model of pulse pileup. The spectrum of the recorded counts in the projection data is corrected using the calibrated forward model, by determining an argument value that optimizes the objective function, the argument being either a corrected X-ray spectrum or the projection lengths of a material decomposition. Images for material components of the material decomposition are then reconstructed using the corrected projection data.