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1.发明申请公开(公告)号：US20230083935A1
公开(公告)日：2023-03-16
申请号：US17469310
申请日：2021-09-08
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Yujie LU , Ilmar HEIN , Zhou YU
Abstract: An apparatus and method to obtain input projection data based on radiation detected at a plurality of detector elements, reconstruct plural uncorrected images in response to applying a reconstruction algorithm to the input projection data, segment the plural uncorrected images into two or more types of material-component images by applying a deep learning segmentation network, generate output projection data corresponding to the two or more types of material-component images based on a forward projection, generate corrected multi material-decomposed projection data based on the generated output projection data corresponding to the two or more types of material-component images, and reconstruct the multi material-component images from the corrected multi material-decomposed projection data to generate one or more corrected images. In some embodiments, the plural uncorrected images are segmented into three or more types of material-component images by applying a deep learning segmentation network and beam hardening correction is performed for the three or more materials.
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公开(公告)号：US20240412426A1
公开(公告)日：2024-12-12
申请号：US18332237
申请日：2023-06-09
Applicant: CANON MEDICAL SYSTEMS CORPORATION
IPC: G06T11/00 , G01N23/046 , G06T3/40 , G06T7/00
Abstract: A method for scatter estimation in a CT including a detector having multiple detector pixels includes: obtaining projection data by scanning an imaging object; reconstructing image data from the projection data; estimating, based on the projection data, a first scatter distribution; selecting, based on the first scatter distribution, a first subset of the pixels; calculating, based on the projection data and the image data, a second scatter distribution with respect to the selected first subset, the second scatter distribution having higher accuracy than the first scatter distribution; acquiring, based on the second scatter distribution, a third scatter distribution with respect to a second subset of the pixels, the third scatter distribution having higher spatial resolution than the second scatter distribution.
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公开(公告)号：US20230274473A1
公开(公告)日：2023-08-31
申请号：US17680873
申请日：2022-02-25
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Rui HUA , Komal DUTTA , Joseph MANAK , Yi HU , Yubing CHANG , Yujie LU , John BAUMGART
CPC classification number: G06T11/005 , G06T5/002 , G06T7/0012 , G01N23/046 , G16H30/40 , G06N5/022 , G06T2207/20084 , G06T2207/10081 , G06T2207/20081 , G06T2207/10116 , G06T2207/30004 , G01N2223/401
Abstract: A projection dataset from a cone beam computed tomography (CBCT) can be input into a first set of one or more neural networks trained for at least one of saturation correction, truncation correction, and scatter correction. Reconstruction can then be performed on the output projection dataset to produce an image dataset. Thereafter, this image dataset can be input into a second set of one or more neural networks trained for at least one of noise reduction and artefact reduction, thereby generating a higher quality CBCT image.
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4.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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Patent Agency Ranking
公开(公告)号：US20210335023A1
公开(公告)日：2021-10-28
申请号：US16860425
申请日：2020-04-28
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Wenyuan QI , Yujie LU , Evren ASMA , Yi QIANG , Jeffrey KOLTHAMMER , Zhou YU
Abstract: The present disclosure relates to an apparatus for estimating scatter in positron emission tomography, comprising processing circuitry configured to acquire an emission map and an attenuation map, each representing an initial image reconstruction of a positron emission tomography scan, calculate, using a radiative transfer equation (RTE) method, a scatter source map of a subject of the positron emission tomography scan based on the emission map and the attenuation map, estimate, using the RTE method and based on the emission map, the attenuation map, and the scatter source map, scatter, and perform an iterative image reconstruction of the positron emission tomography scan based on the estimated scatter and raw data from the positron emission tomography scan of the subject.
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公开(公告)号：US20210007695A1
公开(公告)日：2021-01-14
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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7.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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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：US20220395246A1
公开(公告)日：2022-12-15
申请号：US17345716
申请日：2021-06-11
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Wenyuan QI , Yujie LU , Ryo OKUDA , Evren ASMA , Manabu TESHIGAWARA , Jeffrey KOLTHAMMER
Abstract: The present disclosure is related to removing scatter from a SPECT scan by utilizing a radiative transfer equation (RTE) method. An attenuation map and emission map are acquired for generating scatter sources maps and scatter on detectors using the RTE method. The estimated scatter on detectors can be removed to produce an image of a SPECT scan with less scatter. Both first-order and multiple-order scatter can be estimated and removed. Additionally, scatter caused by multiple tracers can be determined and removed.
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