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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：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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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
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.
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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
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.
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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
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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Patent Agency Ranking
公开(公告)号：US11039806B2
公开(公告)日：2021-06-22
申请号：US16227251
申请日：2018-12-20
Applicant: Canon Medical Systems Corporation
Inventor： Jian Zhou , Ruoqiao Zhang , Zhou Yu , Yan Liu
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：US11238585B2
公开(公告)日：2022-02-01
申请号：US16839733
申请日：2020-04-03
Inventor： Dimple Modgil , Patrick La Riviere , Yan Liu , Zhou Yu
Abstract: A method and apparatus uses multi-material decomposition of three or more material components to generate material-component images from spectral images reconstructed from spectral computed tomography data. In three-component material decomposition e.g., the Mendonça method is used for multi-material decomposition when the attenuation values satisfy an assumed volume fraction condition (i.e., for a given voxel, the attenuation values are within a triangle having vertices given by unit volume fractions of three respective material components). However, when the volume fraction condition fails (e.g., the attenuation values are outside the triangle), either a shortest-Hausdorff-distance method or a closest-edge method is used for multi-material decomposition. For example, the attenuation values of the voxel are projected onto a lower-dimensional space (e.g., the space of a closest edge) and decomposed into a pair/single material component(s) of the lower-dimensional space.
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公开(公告)号：US10803984B2
公开(公告)日：2020-10-13
申请号：US16143161
申请日：2018-09-26
Applicant: Canon Medical Systems Corporation
IPC: G16H30/20 , G06T5/00 , G06T5/50 , G06T11/00 , G06T7/00 , G06K9/62 , G06K9/68 , G06K9/66 , G06N3/08
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.
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公开(公告)号：US20210312612A1
公开(公告)日：2021-10-07
申请号：US16839733
申请日：2020-04-03
Inventor： Dimple MODGIL , Patrick La Riviere , Yan Liu , Zhou Yu
Abstract: A method and apparatus uses multi-material decomposition of three or more material components to generate material-component images from spectral images reconstructed from spectral computed tomography data. In three-component material decomposition e.g., the Mendonça method is used for multi-material decomposition when the attenuation values satisfy an assumed volume fraction condition (i.e., for a given voxel, the attenuation values are within a triangle having vertices given by unit volume fractions of three respective material components). However, when the volume fraction condition fails (e.g., the attenuation values are outside the triangle), either a shortest-Hausdorff-distance method or a closest-edge method is used for multi-material decomposition. For example, the attenuation values of the voxel are projected onto a lower-dimensional space (e.g., the space of a closest edge) and decomposed into a pair/single material component(s) of the lower-dimensional space.
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公开(公告)号：US10945695B2
公开(公告)日：2021-03-16
申请号：US16231189
申请日：2018-12-21
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Abstract: A deep learning (DL) network reduces artifacts in computed tomography (CT) images based on complementary sparse-view projection data generated from a sparse kilo-voltage peak (kVp)-switching CT scan. The DL network is trained using input images exhibiting artifacts and target images exhibiting little to no artifacts. Another DL network can be trained to perform image-domain material decomposition of the artifact-mitigated images by being trained using target images in which beam hardening is corrected and spatial variations in the X-ray beam are accounted for. Further, material decomposition and artifact mitigation can be integrated in a single DL network that is trained using as inputs reconstructed images having artifacts and as targets material images without artifacts with beam-hardening corrections, etc. Further, the target material images can be transformed using a whitening transform to decorrelate noise.
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