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41.发明申请公开(公告)号：US20190164317A1
公开(公告)日：2019-05-30
申请号：US16206892
申请日：2018-11-30
Applicant: CANON MEDICAL SYSTEMS CORPORATION , THE UNIVERSITY OF CENTRAL FLORIDA RESEARCH FOUNDATION, Inc.
Inventor： Qiulin Tang , Alexander Katsevich , Zhou Yu , Wenli Wang
Abstract: An apparatus and method are provided for computed tomography (CT) imaging to reduce truncation artifacts due to a part of an imaged object being outside the scanner field of view (FOV) for at least some views of a CT scan. After initial determining extrapolation widths to extend the projection data to fill a truncation region, the extrapolation widths are combined into a padding map and smoothed to improve uniformity and remove jagged edges. Then a hybrid material model fits the measured projection data nearest the truncation region to extrapolate projection data filling the truncation region. Smoothing the padding map is improved by the insight that in general smaller extrapolation widths are more accurate and trustworthy. Further, practical applications often include multiple inhomogeneous materials. Thus, the hybrid material model provides a better approximation than single material models, and more accurate fitting is achieved.
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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.
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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
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.
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44.发明授权公开(公告)号：US12008689B2
公开(公告)日：2024-06-11
申请号：US17542245
申请日：2021-12-03
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Yujie Lu , Tzu-Cheng Lee , Liang Cai , Jian Zhou , Zhou Yu
CPC classification number: G06T11/005 , G06N3/08 , G16H30/40 , G06T2211/40
Abstract: Devices, systems, and methods obtain first radiographic-image data reconstructed based on a set of projection data acquired in a radiographic scan; apply one or more trained machine-learning models to the set of projection data and the first radiographic-image data to obtain a set of parameters for a scatter kernel; input the set of parameters and the set of projection data into the scatter kernel to obtain scatter-distribution data; and perform scatter correction on the set of projection data using the scatter-distribution data, to obtain a set of corrected projection data.
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45.发明公开公开(公告)号：US20230177745A1
公开(公告)日：2023-06-08
申请号：US17542245
申请日：2021-12-03
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Yujie Lu , Tzu-Cheng Lee , Liang Cai , Jian Zhou , Zhou Yu
CPC classification number: G06T11/005 , G16H30/40 , G06N3/08 , G06T2211/40
Abstract: Devices, systems, and methods obtain first radiographic-image data reconstructed based on a set of projection data acquired in a radiographic scan; apply one or more trained machine-learning models to the set of projection data and the first radiographic-image data to obtain a set of parameters for a scatter kernel; input the set of parameters and the set of projection data into the scatter kernel to obtain scatter-distribution data; and perform scatter correction on the set of projection data using the scatter-distribution data, to obtain a set of corrected projection data.
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Patent Agency Ranking
公开(公告)号：US11315221B2
公开(公告)日：2022-04-26
申请号：US16372174
申请日：2019-04-01
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Masakazu Matsuura , Jian Zhou , Zhou Yu
Abstract: A method and apparatus is provided to perform medical imaging in which feature-aware reconstruction is performed using a neural network. The neural network is trained to perform feature-aware reconstruction by using a training dataset in which the target data has a spatially-dependent degree of denoising and artifact reduction based on the features represented in the image. For example, a target image can be generated by reconstructing multiple images, each using a respective regularization parameter that is optimized for a different anatomy/organ (e.g., abdomen, lung, bone, etc.). And a target image can be generated using artifact reduction method (e.g. metal artifact reduction, aliasing artifact reduction, etc.). Then respective regions/features (e.g., abdomen, lung, and bone, artifact free, regions/features) can be extracted from the corresponding images and combined into a single combined image, which is used as the target data to train the neural network.
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公开(公告)号：US11276209B2
公开(公告)日：2022-03-15
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：US11176428B2
公开(公告)日：2021-11-16
申请号：US16372206
申请日：2019-04-01
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Tzu-Cheng Lee , Jian Zhou , Zhou Yu
Abstract: A method and apparatus is provided to reduce the noise in medical imaging by training a deep learning (DL) network to select the optimal parameters for a convolution kernel of an adaptive filter that is applied in the data domain. For example, in X-ray computed tomography (CT) the adaptive filter applies smoothing to a sinogram, and the optimal amount of the smoothing and orientation of the kernel (e.g., a bivariate Gaussian) can be determined on a pixel-by-pixel basis by applying a noisy sinogram to the DL network, which outputs the parameters of the filter (e.g., the orientation and variances of the Gaussian kernel). The DL network is trained using a training data set including target data (e.g., the gold standard) and input data. The input data can be sinograms generated by a low-dose CT scan, and the target data generated by a high-dose CT scan.
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公开(公告)号：US11100684B2
公开(公告)日：2021-08-24
申请号：US16509408
申请日：2019-07-11
Applicant: Canon Medical Systems Corporation
Inventor： Ilmar Hein , Zhou Yu , Ting Xia
Abstract: A method and apparatus are provided that use deep learning (DL) networks to reduce noise and artifacts in reconstructed computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI) images. DL networks are used in both the sinogram and image domains. In each domain, a detection network is used to (i) determine if particular types of artifacts are exhibited (e.g., beam-hardening artifact, ring, motion, metal, photon-starvation, windmill, zebra, partial-volume, cupping, truncation, streak artifact, and/or shadowing artifacts), (ii) determine whether the detected artifact can be corrected through a changed scan protocol or image-processing techniques, and (iii) determine whether the detected artifacts are fatal, in which case the scan is stopped short of completion. When the artifacts can be corrected, corrective measures are taken through a changed scan protocol or through image processing to reduce the artifacts (e.g., convolutional neural network can be trained to perform the image processing).
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50.发明授权公开(公告)号：US11087508B2
公开(公告)日：2021-08-10
申请号：US16206922
申请日：2018-11-30
Applicant: CANON MEDICAL SYSTEMS CORPORATION , THE UNIVERSITY OF CENTRAL FLORIDA RESEARCH FOUNDATION, Inc.
Inventor： Alexander Katsevich , Zhou Yu , Daxin Shi
IPC: G06T11/00
Abstract: A method and apparatus is provided to reconstruct a computed tomography image using iterative reconstruction (IR) that is accelerated using various combinations of ordered subsets, conjugate gradient, preconditioning, resetting/restarting, and/or gradient approximation techniques. For example, when restarting criteria are satisfied the IR algorithm can be reset by setting conjugate-gradient parameters to initial values and/or by changing the number of ordered subsets. The IR algorithm can be accelerated by approximately calculating the gradients, by using a diagonal or Fourier preconditioner, and by selectively updating the preconditioner based on the regularization function. The update direction and step size can be calculated using the preconditioner and a surrogate function, which is not necessarily separable.
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