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1.发明申请公开(公告)号：US20200242770A1
公开(公告)日：2020-07-30
申请号：US16845402
申请日：2020-04-10
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Yiemeng HOI , Joseph MANAK , Kazumasa ARAKITA , Jingwu YAO , James BEGELMAN , Victor GORIN
Abstract: A medical image processing apparatus according to an embodiment includes processing circuitry. The processing circuitry acquires image data including image data of a blood vessel of a subject. The processing circuitry performs analysis related to the blood vessel by using the image data, and specifies a region of interest in the blood vessel based on a result of the analysis. The processing circuitry performs fluid analysis on a region other than the region of interest at a first accuracy, and performs fluid analysis on the region of interest at a second accuracy that is higher than the first accuracy.
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公开(公告)号：US20240374230A1
公开(公告)日：2024-11-14
申请号：US18316283
申请日：2023-05-12
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： John BAUMGART , Yi HU , Joseph MANAK , Haruki IWAI
Abstract: A method of adjusting components in an imaging apparatus includes obtaining a first image, the first image being acquired by performing a scan using a first set of scan parameters; analyzing the first image to detect objects in the first image and corresponding features of the detected objects; based on a detection result of a first object having a corresponding first object feature in the first image, determining an action corresponding to the first object and the first object feature; determining an updated set of scan parameters based on the determined action; and controlling the imaging apparatus based on the updated set of scan parameters.
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公开(公告)号：US20240087111A1
公开(公告)日：2024-03-14
申请号：US17940486
申请日：2022-09-08
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Yi HU , Yu-bing CHANG , Haruki IWAI , Joseph MANAK , John BAUMGART , James BEGELMAN
CPC classification number: G06T7/0012 , A61B6/5258 , G06T2207/10081 , G06T2207/30168
Abstract: A system and method is provided for controlling an acquisition system to obtain, based on a currently acquired set of projection data, a next set of projection data that is to be acquired using a set of learned imaging conditions. The set of learned imaging conditions are, at least in part, based on training data including projection data and dose information. In one embodiment, the initial dose and angle for obtaining the initial set of projection data are chosen at random. In another embodiment, the initial dose and angle for obtaining the initial set of projection data are chosen based on a test scan and a trained neural network for outputting the initial dose and angle using a loss function.
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公开(公告)号：US20230206427A1
公开(公告)日：2023-06-29
申请号：US17563536
申请日：2021-12-28
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Andrew JAMES WHEATON , Joseph MANAK , Bradley STEPHENS
CPC classification number: G06T7/0012 , G06N20/10 , G06T7/11 , G06T2207/10088 , G06T2207/20081
Abstract: An apparatus for magnetic resonance imaging includes processing circuitry to obtain a set of sequence instructions for performing a magnetic resonance scan; partition the obtained set of sequence instructions into a plurality of kernels by determining partition time points defining boundaries of the plurality of kernels, wherein the partition time points are not equally spaced in time; convert a first kernel of the plurality of kernels into a first hardware instruction set; transmit the first hardware instruction set to a hardware board controller for execution; and reconstruct a magnetic resonance image from received data, including data obtained by executing the first kernel.
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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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Patent Agency Ranking
公开(公告)号：US20230115941A1
公开(公告)日：2023-04-13
申请号：US17962734
申请日：2022-10-10
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Yi HU , Yu-Bing CHANG , Komal DUTTA , Haruki IWAI , Rui HUA , Joseph MANAK
Abstract: An X-ray diagnostic apparatus according to an embodiment includes processing circuitry configured to improve quality of fourth data corresponding to a fourth number of views that is smaller than a first number of views by inputting the fourth data to a learned model generated by performing machine learning with second data corresponding to a second number of views as input learning data, and third data corresponding to a third number of views that is larger than the second number of views as output learning data, the second data and the third data being acquired based on first data corresponding to the first number of views. The fourth data is data acquired by tomosynthesis imaging.
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公开(公告)号：US20220414832A1
公开(公告)日：2022-12-29
申请号：US17356612
申请日：2021-06-24
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Yi HU , Rui HUA , Joseph MANAK , John BAUMGART , Yu-Bing CHANG
Abstract: A general workflow for deep learning based image restoration in X-ray and fluoroscopy/fluorography is disclosed. Higher quality images and lower quality images are generated as training data. This training data can further be categorized by anatomical structure. This training data can be used to train a learned model, such as a neural network or deep-learning neural network. Once trained, the learned model can be used for real-time inferencing. The inferencing can be more further improved by employing a variety of techniques, including pruning the learned model, reducing the precision of the learned mode, utilizing multiple image restoration processors, or dividing a full size image into snippets.
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8.发明申请公开(公告)号：US20220296197A1
公开(公告)日：2022-09-22
申请号：US17206866
申请日：2021-03-19
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： John BAUMGART , Joseph MANAK
Abstract: A system and a method by which multiple regions or objects of interest can be indicated within an X-ray image, from which a user can select a primary region or object of interest and accordingly adjust the appropriate X-ray dose for obtaining a better quality image of the selected regions or objects of interest.
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9.发明申请公开(公告)号：US20250054153A1
公开(公告)日：2025-02-13
申请号：US18925738
申请日：2024-10-24
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Yiemeng HOI , Joseph MANAK , Kazumasa ARAKITA , Jingwu YAO , James BEGELMAN , Victor GORIN
Abstract: A medical image processing apparatus according to an embodiment includes processing circuitry. The processing circuitry acquires image data including image data of a blood vessel of a subject. The processing circuitry performs analysis related to the blood vessel by using the image data, and specifies a region of interest in the blood vessel based on a result of the analysis. The processing circuitry performs fluid analysis on a region other than the region of interest at a first accuracy, and performs fluid analysis on the region of interest at a second accuracy that is higher than the first accuracy.
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公开(公告)号：US20240303780A1
公开(公告)日：2024-09-12
申请号：US18181635
申请日：2023-03-10
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Yi HU , Shijie LI , John BAUMGART , Joseph MANAK , Kunio SHIRAISHI , Saki HASHIMOTO
IPC: G06T5/00
CPC classification number: G06T5/70 , G06T5/77 , G06T2207/10016 , G06T2207/10081 , G06T2207/10121 , G06T2207/20021 , G06T2207/20081 , G06T2207/20084
Abstract: A medical image processing method, an X-ray diagnostic apparatus, and a method of generating a learned model includes receiving first X-ray image data, inputting the first X-ray image data to a trained model, outputting, from the trained model, an X-ray image having an image quality higher than an image quality of the first X-ray image data. The learned model was trained using contrastive learning using second X-ray image data as input data, third X-ray image data and fourth X-ray image data as label data, the third X-ray image data being negative label data having worse image quality than the second X-ray image data, and the fourth image data being positive label data having better image quality than the second X-ray image data.
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