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1.发明公开公开(公告)号：US20230326596A1
公开(公告)日：2023-10-12
申请号：US17718898
申请日：2022-04-12
Applicant: CANON MEDICAL SYSTEMS CORPORATION
IPC: G16H50/20 , G06T7/00 , G06T11/00 , G06T7/11 , G06T7/62 , G16H30/40 , G06N3/08 , A61B6/12 , A61B6/03 , A61B6/00
CPC classification number: G16H50/20 , G06T7/0012 , G06T11/008 , G06T7/11 , G06T7/62 , G16H30/40 , G06N3/08 , A61B6/12 , A61B6/032 , A61B6/5258 , G06T2207/20081 , G06T2207/20084 , G06T2207/30052 , G06T2207/10081
Abstract: A method of processing information acquired by imaging performed by a medical image diagnostic apparatus, the method including but not limited to at least one of (A) acquiring a training image volume including at least one three-dimensional object having an embedded three-dimensional feature having a first cross-sectional area in a first three-dimensional plane; selecting a second cross-sectional area in a second three-dimensional plane containing the embedded three-dimensional feature, wherein the second cross-sectional area is larger than the first cross-sectional area; and training an untrained neural network with an image of the second cross-sectional area generated from the training image volume; and (B) acquiring a first set of training data; determining a first distribution of tissue density information from the first set of training data; generating from the first set of training data a second set of training data by performing at least one of a tissue-density shifting process and a tissue-density scaling process; and training an untrained neural network with the first and second sets of training data to obtain a trained neural network.
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2.发明公开公开(公告)号：US20230215058A1
公开(公告)日：2023-07-06
申请号：US17723894
申请日：2022-04-19
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Qiulin TANG , Thomas LABNO , Jian ZHOU , Liang CAI , Zhou YU
CPC classification number: G06T11/005 , A61B6/032 , A61B6/5205 , G16H30/20 , G06T2210/41
Abstract: A method, system, and computer readable medium to compensate for consecutive missing views in Computed Tomography (CT) reconstruction. By utilizing at least one complementary ray from a previous or subsequent view, the missing view(s) can be filled in. When plural complementary rays exist, a linear or non-linear combination of rays can be used to fill in the missing views, and the weights used in the combination may be smoothed to prevent over-emphasis of the replacement views.
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公开(公告)号：US20230011759A1
公开(公告)日：2023-01-12
申请号：US17369596
申请日：2021-07-07
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Yujie LU , Qiulin TANG , Zhou YU , Jian ZHOU
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.
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公开(公告)号：US20210118098A1
公开(公告)日：2021-04-22
申请号：US17013104
申请日：2020-09-04
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Chung CHAN , Jian ZHOU , Evren ASMA
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.
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公开(公告)号：US20200323508A1
公开(公告)日：2020-10-15
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：US20250157100A1
公开(公告)日：2025-05-15
申请号：US18509385
申请日：2023-11-15
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Qiulin TANG , Jian ZHOU , Liang CAI , Chih-Chieh LIU
Abstract: A medical image processing method includes obtaining a set of projection data acquired in a computed tomography (CT) scan of a three-dimensional region of an object to be examined; generating for each time point of a plurality of time points of the CT scan based on a part of the obtained set of projection data corresponding to the time point, a pair of feature maps for estimating motion at the time point so as to generate a plurality of pairs of feature maps, each feature map representing a feature of an image reconstructed from the part of the obtained set of projection data; estimating, based on the generated plurality of pairs of feature maps, a four-dimensional motion field; and reconstructing, based on the estimated four-dimensional motion field and the obtained set of projection data, a CT image of the object.
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公开(公告)号：US20240032877A1
公开(公告)日：2024-02-01
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 , G01N23/046
CPC classification number: A61B6/032 , A61B6/54 , G01N23/046 , A61B6/503
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.
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8.发明公开公开(公告)号：US20230284997A1
公开(公告)日：2023-09-14
申请号：US17692697
申请日：2022-03-11
Applicant: CANON MEDICAL SYSTEMS CORPORATION
CPC classification number: A61B6/544 , G06T11/008 , A61B6/5258 , G06T2210/41 , G06T2200/04 , A61B6/025
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.
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9.发明申请公开(公告)号：US20220139006A1
公开(公告)日：2022-05-05
申请号：US17577689
申请日：2022-01-18
Applicant: CANON MEDICAL SYSTEMS CORPORATION
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.
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公开(公告)号：US20210290193A1
公开(公告)日：2021-09-23
申请号：US17339093
申请日：2021-06-04
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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