公开(公告)号：US20220323035A1

公开(公告)日：2022-10-13

申请号：US17229247

申请日：2021-04-13

Applicant: CANON MEDICAL SYSTEMS CORPORATION

Inventor： Qiulin Tang ,  Liang Cai ,  Zhou Yu ,  Jian Zhou

IPC: A61B6/00 ,  G06T11/00 ,  G06T7/11

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.

公开(公告)号：US20220156919A1

公开(公告)日：2022-05-19

申请号：US16951931

申请日：2020-11-18

Applicant: CANON MEDICAL SYSTEMS CORPORATION

Inventor： Qiulin Tang ,  Jian Zhou ,  Zhou Yu

IPC: G06T7/00 ,  G16H50/50 ,  G16H30/40 ,  G16H50/20 ,  G16H50/70 ,  G06N3/08 ,  G06T7/12 ,  G06T11/00 ,  A61B6/03 ,  A61B6/00

Abstract: Devices, systems, and methods for generating a medical image obtain scan data that were generated by scanning a scanned region, wherein the scan data include groups of scan data that were captured at respective angles; generate partial reconstructions of at least a part of the scanned region, wherein each partial reconstruction of the partial reconstructions is generated based on a respective one or more groups of the groups of scan data, and wherein a collective scanning range of the respective one or more groups is less than the angular scanning range; input the partial reconstructions into a machine-learning model, which generates one or more motion-compensated reconstructions of the at least part of the scanned region based on the partial reconstructions; calculate a respective edge entropy of each of the one or more motion-compensated reconstructions of the at least part of the scanned region; and adjust the machine-learning model based on the respective edge entropies.

公开(公告)号：US11234666B2

公开(公告)日：2022-02-01

申请号：US16258396

申请日：2019-01-25

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：US11013487B2

公开(公告)日：2021-05-25

申请号：US15929155

申请日：2019-10-18

Applicant: CANON MEDICAL SYSTEMS CORPORATION

Inventor： Jian Zhou ,  Xiaohui Zhan ,  Zhou Yu

IPC: A61B6/00 ,  A61B6/03 ,  G01T1/24

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). The forward model represents pulse pileup effects using an integral in which the integrand includes a term that is a function of a count rate, which term is called a spectrum distortion correction function. This correction function can be represented as superposition of basis energy functions and corresponding polynomials of the count rate, which are defined by the polynomial coefficients. To calibrate the forward model, the polynomial coefficients are adjusted to optimize an objective function, which uses calibration data having known projections lengths for the material components of a material decomposition. To determine projection lengths for projection data from a computed tomography scan, the calibrated polynomial coefficients are held constant and the projection lengths are adjusted to optimize an objective function.

公开(公告)号：US10925568B2

公开(公告)日：2021-02-23

申请号：US16510632

申请日：2019-07-12

Applicant: CANON MEDICAL SYSTEMS CORPORATION

Inventor： Yujie Lu ,  Zhou Yu ,  Jian Zhou

IPC: G06K9/00 ,  A61B6/00 ,  A61B6/03 ,  G06T11/00 ,  G06N20/00 ,  G06N3/08

Abstract: A method and apparatus is provided that uses a deep learning (DL) network to improve the image quality of computed tomography (CT) images, which were reconstructed using an analytical reconstruction method. The DL network is trained to use physical-model information in addition to the analytical reconstructed images to generate the improved images. The physical-model information can be generated, e.g., by estimating a gradient of the objective function (or just the data-fidelity term) of a model-based iterative reconstruction (MBIR) method (e.g., by performing one or more iterations of the MBIR method). The MBIR method can incorporate physical models for X-ray scatter, detector resolution/noise/non-linearities, beam-hardening, attenuation, and/or the system geometry. The DL network can be trained using input data comprising images reconstructed using the analytical reconstruction method and target data comprising images reconstructed using the MBIR method.

公开(公告)号：US20190313993A1

公开(公告)日：2019-10-17

申请号：US15951329

申请日：2018-04-12

Applicant: CANON MEDICAL SYSTEMS CORPORATION

Inventor： Jian Zhou ,  Zhou Yu ,  Yan Liu

IPC: A61B6/00 ,  A61B6/02 ,  A61B6/04

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.

公开(公告)号：US20190108904A1

公开(公告)日：2019-04-11

申请号：US16143161

申请日：2018-09-26

Applicant: Canon Medical Systems Corporation

Inventor： Jian Zhou ,  Zhou Yu ,  Yan Liu

IPC: G16H30/20 ,  G06N3/08 ,  G06T5/00 ,  G06T5/50 ,  G06T11/00 ,  G06T7/00 ,  G06K9/62 ,  G06K9/68 ,  G06K9/66

CPC classification number: G16H30/20 ,  G06K9/6298 ,  G06K9/66 ,  G06K9/6814 ,  G06N3/08 ,  G06T5/002 ,  G06T5/50 ,  G06T7/0014 ,  G06T11/003 ,  G06T11/008 ,  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：US12178631B2

公开(公告)日：2024-12-31

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

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：US12138090B2

公开(公告)日：2024-11-12

申请号：US17875051

申请日：2022-07-27

Applicant: CANON MEDICAL SYSTEMS CORPORATION

Inventor： Chih-chieh Liu ,  Jian Zhou ,  Qiulin Tang ,  Liang Cai ,  Zhou Yu

IPC: A61B6/03 ,  A61B6/00 ,  A61B6/50 ,  G01N23/046

Abstract: An information processing method controls a CT scanner such that the method includes, but is not limited to, determining an X-ray irradiation period from an electrocardiogram acquired from an electrocardiography device attached to a living object to be imaged, by processing the electrocardiogram at multiple different cardiac phases; performing, by controlling a CT gantry including and rotatably supporting an X-ray source and an X-ray detector, a diagnostic CT scan in the determined X-ray irradiation period, of at least a part of the heart region, to obtain a CT image; and causing a display unit to display the obtained CT image. The method can be performed at least by an information processing apparatus including processing circuitry and/or computer instructions stored in a non-transitory computer readable storage medium for performing the method.

公开(公告)号：US12067651B2

公开(公告)日：2024-08-20

申请号：US17462391

申请日：2021-08-31

Applicant: CANON MEDICAL SYSTEMS CORPORATION

Inventor： Qiulin Tang ,  Ruoqiao Zhang ,  Jian Zhou ,  Zhou Yu

IPC: G06T11/00 ,  G06N3/08 ,  G06T7/00

CPC classification number: G06T11/003 ,  G06N3/08 ,  G06T7/0012 ,  G06T2207/20081 ,  G06T2207/20084

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.