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公开(公告)号：US20240041417A1
公开(公告)日：2024-02-08
申请号：US18230835
申请日：2023-08-07
Inventor： Ruogu Fang , Garrett Carlton Fullerton , Simon Kato
CPC classification number: A61B6/507 , A61B6/032 , A61B6/501 , G06T11/008 , A61B6/461
Abstract: Various examples are provided related to predicting perfusion images from non-contrast scans. In one example, a method for predicting perfusion images includes generating perfusion maps of an organ of a subject from non-contrast computed tomography (NCCT) slices of the organ; processing the perfusion maps based upon weights determined by a Physicians-in-the-Loop (PILO) module; and generating synthetic computed tomography perfusion (CTP) maps from the processed perfusion maps, the synthetic CTP maps generated by deep learning-based multimodal image translation. In another example, a system includes at least one computing device that can generate prefusion maps of an organ from NCCT slices; process the perfusion maps based upon weights determined by a PILO module; and generate synthetic CTP maps from the processed perfusion maps using deep learning-based multimodal image translation. The CTP maps can be rendered for display to a user.
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Patent Agency Ranking
公开(公告)号：US20230301614A1
公开(公告)日：2023-09-28
申请号：US18007366
申请日：2021-07-29
Inventor： Ruogu Fang , Peng Liu
CPC classification number: A61B6/5258 , A61B6/5235 , A61B6/583 , G06T5/002 , G06T5/50 , G06T2207/30004 , G06T2207/30168 , G06T2207/20081
Abstract: Various examples are provided related to reconstructing images such as, e.g., medical images from low-dose image scans. Adversarial learning such as, e.g., a Cyclic Simulation and Denoising (CSD) framework can be used to address challenges of complicated mixed noise in real low-dose scans. The CSD framework can include a simulator model that can extract low-dose noise and features (e.g., tissue features) from separate image spaces into a unified feature space and a denoiser model that can learn how to remove noise and restore features, simultaneously. Both the simulator model and the denoiser model can regularize each other in a cyclic manner to optimize network learning effectively. The CSD framework in combination with phantom scans can embrace the realistic low-dose noise and features into a unified learning environment to address the challenge of real low-dose image restoration.
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3.发明申请公开(公告)号：US20250057424A1
公开(公告)日：2025-02-20
申请号：US18720869
申请日：2022-12-16
Inventor： Ruogu Fang , Kyle B. See , Stephen Coombes
Abstract: Various examples are provided related to dynamic brain parcellation. In one example, a method for functional task prediction with dynamic supervoxel parcellation includes preprocessing activation data obtained from brains of multiple subjects to generate one or more dynamic parcellated supervoxel maps of the brain, the activation data associated with a functional task, and determining an anatomical location of the functional task in the brain of another subject based upon classification of supervoxels of the one or more dynamic parcellated supervoxel maps. In another example, a system includes at least one computing device that can preprocess activation data to generate one or more dynamic parcellated supervoxel maps of the brain, the activation data associated with a functional task, and determine an anatomical location of the functional task based upon classification of supervoxels of the one or more dynamic parcellated supervoxel maps.
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4.发明公开公开(公告)号：US20230141896A1
公开(公告)日：2023-05-11
申请号：US17915362
申请日：2021-03-23
Inventor： Peng Liu , Ruogu Fang
CPC classification number: A61B3/0025 , A61B3/12 , G06V10/82 , G06V40/197
Abstract: Embodiments of the present disclosure are directed to training a neural network for ocular cup (OC) or ocular disc (OD) detection. One such method comprises initiating training of a first network to learn detection of OC/OD regions within a labeled source sample from a source domain; sharing training weights of the first network with a second network; initiating training of the second network to learn detection of OC/OD regions within an unlabeled sample from a target domain; transferring average training weights of the second network to a third network; initiating training of the third network to learn detection of OC/OD regions within an unlabeled sample from the target domain; computing a mean square error loss between the third network and the second network for a same target sample; and adjusting training weights of the second network based on the mean square error loss computation.
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公开(公告)号：US11069033B2
公开(公告)日：2021-07-20
申请号：US16558779
申请日：2019-09-03
Inventor： Ruogu Fang , Peng Liu
Abstract: Various embodiments for image denoising using a convolutional neural network (CCN) are described. A system may include at least one computing device and program instructions stored in memory and executable in the at least one computing device that, when executed, direct the at least one computing device to implement a genetic algorithm (GA) routine that identifies and optimizes a plurality of hyperparameters for use in denoising an image using the convolutional neural network. An image may be denoised using the convolutional neural network, where the image is denoised using the hyperparameters identified and optimized in the genetic algorithm routine.
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6.发明申请公开(公告)号：US20200082507A1
公开(公告)日：2020-03-12
申请号：US16558779
申请日：2019-09-03
Inventor： Ruogu Fang , Peng Liu
Abstract: Various embodiments for image denoising using a convolutional neural network (CCN) are described. A system may include at least one computing device and program instructions stored in memory and executable in the at least one computing device that, when executed, direct the at least one computing device to implement a genetic algorithm (GA) routine that identifies and optimizes a plurality of hyperparameters for use in denoising an image using the convolutional neural network. An image may be denoised using the convolutional neural network, where the image is denoised using the hyperparameters identified and optimized in the genetic algorithm routine.
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