-
1.发明授权公开(公告)号：US12205199B2
公开(公告)日：2025-01-21
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.
-
公开(公告)号：US12062153B2
公开(公告)日：2024-08-13
申请号：US17369596
申请日：2021-07-07
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Yujie Lu , Qiulin Tang , Zhou Yu , Jian Zhou
CPC classification number: G06T5/20 , G06N3/08 , G06T5/50 , G16H30/40 , G06T2200/04 , G06T2207/10116 , G06T2207/20081 , G06T2207/20084 , G06T2207/30004
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.
-
3.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.
-
公开(公告)号：US20250005818A1
公开(公告)日：2025-01-02
申请号：US18343608
申请日：2023-06-28
Applicant: CANON MEDICAL SYSTEMS CORPORATION
IPC: G06T11/00
Abstract: According to some embodiments, a method comprises obtaining a group of reconstructed-image data; converting the group of reconstructed-image data to a derivative of Radon space, thereby generating Radon-space data, wherein the Radon-space data have a radial sampling pattern in the derivative of Radon space; and generating resampled data by inputting the Radon-space data into a first trained machine-learning model for resampling Radon-space data, wherein the resampled data have a cone-beam-projection-geometry-shaped sampling pattern in the derivative of Radon space.
-
5.发明申请公开(公告)号：US20200340932A1
公开(公告)日：2020-10-29
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 , G06N3/08
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).
-
Patent Agency Ranking
公开(公告)号：US11241211B2
公开(公告)日：2022-02-08
申请号：US16816953
申请日：2020-03-12
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Wenyuan Qi , Yi Qiang , Karthikayan Balakrishnan , Yujie Lu
Abstract: A method and apparatus is provided to perform dead-time correction in a positron emission tomography (PET) by estimating a full singles spectrum using a scatter model. The scatter model can use a Monte Carlo method, a radiation transfer equation method, an artificial neural network, or an analytical expression. The scatter model simulates scatter based on an emission image/map and an attenuation image/map to estimate Compton scattering. In the full singles spectrum, the singles counts with energies less than 511 keV are determined from the simulated scatter. The attenuation image can be generated based on X-ray computed tomography or based on applying a joint-estimation to PET data.
-
公开(公告)号：US20210282732A1
公开(公告)日：2021-09-16
申请号：US16816953
申请日：2020-03-12
Applicant: Canon Medical Systems Corporation
Inventor： Wenyuan QI , Yi Qiang , Karthikayan Balakrishnan , Yujie Lu
Abstract: A method and apparatus is provided to perform dead-time correction in a positron emission tomography (PET) by estimating a full singles spectrum using a scatter model. The scatter model can use a Monte Carlo method, a radiation transfer equation method, an artificial neural network, or an analytical expression. The scatter model simulates scatter based on an emission image/map and an attenuation image/map to estimate Compton scattering. In the full singles spectrum, the singles counts with energies less than 511 keV are determined from the simulated scatter. The attenuation image can be generated based on X-ray computed tomography or based on applying a joint-estimation to PET data.
-
公开(公告)号：US20240144551A1
公开(公告)日：2024-05-02
申请号：US18049953
申请日：2022-10-26
Applicant: CANON MEDICAL SYSTEMS CORPORATION
CPC classification number: G06T11/005 , G06T7/0012 , G06T2207/10081 , G06T2207/20081 , G06T2207/20084 , G06T2207/30008
Abstract: According to some embodiments, a method comprises obtaining a set of projection data acquired from a CT scan of an object; generating, based on the set of projection data, one or more sets of preliminary scattering data; and performing X-ray scatter correction by inputting the obtained set of projection data and the generated one or more sets of preliminary scattering data into a trained machine-learning model for extracting X-ray scatter components from the set of projection data.
-
9.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).
-
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.
-
-
-
-
-
-
-
-
-
Search Results
15
records
(took 0.019 seconds)
