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公开(公告)号：US10755395B2
公开(公告)日：2020-08-25
申请号：US14953221
申请日：2015-11-27
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Zhou Yu , Qiulin Tang , Satoru Nakanishi , Wenli Wang
Abstract: An apparatus and method of denoising a dynamic image is provided. The dynamic image can represent a time-series of snapshot images. The dynamic image is transformed, using a sparsifying transformation, into an aggregate image and a series of transform-domain images. The transform-domain images represent kinetic information of the dynamic images (i.e., differences between the snapshots), and the aggregate image represents static information (i.e., features and structure common among the snapshots). The transform-domain images, which can be approximated using a sparse approximation method, are denoised. The denoised transform-domain images are recombined with the aggregate image using an inverse sparsifying transformation to generate a denoised dynamic image. The transform-domain images can be denoised using at least one of a principal component analysis method and a K-SVD method.
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22.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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23.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：US11864939B2
公开(公告)日：2024-01-09
申请号：US17339093
申请日：2021-06-04
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Jian Zhou , Ruoqiao Zhang , Zhou Yu , Yan Liu
CPC classification number: A61B6/5258 , A61B6/4014 , G06N3/045 , G06N3/084 , G06T5/10 , G06T5/20 , G06T5/50 , G06T11/005 , G06T11/006 , G06T11/008 , A61B6/482 , A61B6/5205 , G06T2207/10081 , G06T2207/20064 , G06T2207/20081 , G06T2207/20084 , G06T2211/408
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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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
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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Patent Agency Ranking
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.
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27.发明授权公开(公告)号：US11060987B2
公开(公告)日：2021-07-13
申请号：US16392177
申请日：2019-04-23
Applicant: Canon Medical Systems Corporation
Inventor： Yujie Lu , Zhou Yu , Richard Thompson
IPC: G01N23/046 , G06N20/00 , G06N3/04 , G06N3/08
Abstract: X-ray scatter simulations to correct computed tomography (CT) data can be accelerated using a non-uniform discretization of the RTE, reducing the number of computations without sacrificing precision. For example, a coarser discretization can be used for higher-order/multiple-scatter flux, than for first-order-scatter flux. Similarly, precision is preserved when coarser angular resolution is used to simulate scatter within a patient, and finer angular resolution used for the scatter flux incident on detectors. Finer energy resolution is more beneficial at lower X-ray energies, and coarser spatial resolution can be applied to regions exhibiting less X-ray scatter (e.g., air and regions with low radiodensity). Further, predefined non-uniform discretization can be learned from scatter simulations on training data (e.g., a priori compressed grids learned from non-uniform grids generated by adaptive mesh methods).
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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
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.
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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
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.
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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
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.
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