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公开(公告)号：US11756161B2
公开(公告)日：2023-09-12
申请号：US17340117
申请日：2021-06-07
Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY
Inventor： Zhanli Hu , Hairong Zheng , Na Zhang , Xin Liu , Dong Liang , Yongfeng Yang , Hanyu Sun
CPC classification number: G06T5/00 , A61B6/037 , G06N3/045 , G06N3/08 , G06T2207/10088 , G06T2207/10104 , G06T2207/20081
Abstract: The present application relates to a method and system for generating multi-task learning-type generative adversarial network for low-dose PET reconstruction, and relates to the field of deep learning. The method includes connecting layers of the encoder with layers of the decoder by skip connection to provide a U-Net type picture generator; generating a group of generative adversarial networks by matching a plurality of picture generators with a plurality of discriminators in one-to-one manner; obtaining a first multi-task learning-type generative adversarial network; designing a joint loss function 1 for improving image quality; and training the first multi-task learning-type generative adversarial network according to the joint loss function 1 in combination with an optimizer to provide a second multi-task learning-type generative adversarial network.
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公开(公告)号：US11915401B2
公开(公告)日：2024-02-27
申请号：US17284794
申请日：2020-12-09
Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY
Inventor： Dong Liang , Zhanli Hu , Hairong Zheng , Xin Liu , Qingneng Li , Yongfeng Yang
CPC classification number: G06T5/50 , G06T3/4046 , G06T7/0012 , G16H30/40 , G06T2207/10081 , G06T2207/10088 , G06T2207/10104 , G06T2207/20076 , G06T2207/20081 , G06T2207/20084 , G06T2207/20212 , G06T2207/30004
Abstract: An apriori guidance network for multitask medical image synthesis is provided. The apriori guidance network includes a generator and a discriminator, wherein the generator includes an apriori guidance module configured to convert an input feature map into a target modal image pointing to a target domain according to an apriori feature, and the apriori feature is a deep feature of the target modal image. The generator is configured to generate a corresponding target domain image by taking the apriori feature of the target modal image and source modal image data as an input. The discriminator is configured to discriminate an authenticity of the target domain image outputted by the generator.
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Patent Agency Ranking
公开(公告)号：US11210783B2
公开(公告)日：2021-12-28
申请号：US16910074
申请日：2020-06-24
Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY
Inventor： Hairong Zheng , Xin Liu , Na Zhang , Zhanli Hu , Dong Liang , Yongfeng Yang
Abstract: A method of processing plaques in magnetic resonance imaging of vessel wall include: step S101, training a generative adversarial network and a capsule neural network to obtain a trained generator network and a trained capsule neural network; and step S102, cascade-connecting the trained generator network with the capsule neural network into a system to recognize and classify plaques in magnetic resonance imaging of vessel wall. In one aspect, the capsule neural network has more abundant vascular plaques characteristic information represented by vector; in another aspect, when the trained generator network and the capsule neural network are cascaded into the system to recognize and classify the plaques in magnetic resonance imaging of vessel wall, an accuracy of recognition and classification may be greatly improved. A device for processing the method as well as a computer for implementing are also disclosed.
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公开(公告)号：US12190568B2
公开(公告)日：2025-01-07
申请号：US17889189
申请日：2022-08-16
Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY
Inventor： Hairong Zheng , Xin Liu , Na Zhang , Zhanli Hu , Qihang Chen , Dong Liang , Yongfeng Yang
IPC: G06V10/774 , A61B5/055 , G06T11/00 , G06V10/42
Abstract: Methods and apparatuses for training a magnetic resonance imaging model, electronic devices and computer readable storage media are provided. A method may include: acquiring a magnetic resonance image data set; constructing a ring deep neural network to be trained; inputting an under-sampled magnetic resonance image and a full-sampled magnetic resonance image respectively to two neural networks included in the ring deep neural network, to generate respective simulated magnetic resonance images; inputting a first simulated full-sampled magnetic resonance image and the full-sampled magnetic resonance image to a pre-constructed first simulated magnetic resonance image class discrimination model, to obtain a first discrimination result indicating whether or not the first simulated full-sampled magnetic resonance image is of a simulated magnetic resonance image class; and adjusting a network parameter of the ring deep neural network based on a preset loss function, to obtain a trained magnetic resonance imaging model.
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公开(公告)号：US12153175B2
公开(公告)日：2024-11-26
申请号：US17824453
申请日：2022-05-25
Inventor： Zhanli Hu , Yongfeng Yang , Chunhui Zhang , Zhonghua Kuang , Xiaohui Wang , San Wu , Dong Liang , Xin Liu , Hairong Zheng
Abstract: A method for processing positron emission tomography data is provided, this method includes: obtaining a first coordinate and a second coordinate respectively corresponding to two ends of a response line to be processed; determining corresponding dimensional coordinates of the response line to be processed in a sinogram based on the first coordinate and the second coordinate; and generating the sinogram corresponding to the response line to be processed based on the dimensional coordinates. According to this method, the amount of calculation of system matrix is reduced, the accuracy of position information of the generated response line is improved, and the accuracy of generated sinogram is improved accordingly.
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公开(公告)号：US12020351B2
公开(公告)日：2024-06-25
申请号：US17433081
申请日：2020-12-18
Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY
Inventor： Zhanli Hu , Hairong Zheng , Dong Liang , Xin Liu , Yongfeng Yang , Dongfang Gao
CPC classification number: G06T11/006 , A61B6/037 , G06T11/008 , G06V10/7715 , G06T2211/40
Abstract: A method, device and equipment for reconstructing a PET image are provided. The method includes acquiring a prior image comprising an anatomical image and an autocorrelation feature image, the autocorrelation feature image being determined based on gray-level co-occurrence matrix of the anatomical image; and acquiring a feature value of the prior image; reconstructing the PET image according to the feature value and an iterative algorithm.
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公开(公告)号：US20210366168A1
公开(公告)日：2021-11-25
申请号：US17287040
申请日：2019-01-15
Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY
Inventor： Zhanli Hu , Yongfeng Yang , Dong Liang , Xin Liu , Hairong Zheng
Abstract: The present invention discloses a PET image reconstruction method, a computer storage medium, and a computer device. The method includes: step 1, obtaining projection data Y and a system matrix P of a PET image; step 2, constructing an imaging model equation Y=PX, in which X is a reconstructed PET image; step 3, obtaining the initial reconstructed image X, and iteratively updating the initial reconstructed image X according to a first objective function to obtain a first reconstructed image; step 4, iteratively updating the first reconstructed image according to the second objective function to obtain the second reconstructed image; and step 5, determining whether an iteration condition is satisfied, if yes, outputting the current round of iteration to obtain the second reconstructed image as a final PET reconstructed image, and if not, returning to step 3 and using the second reconstructed image in the current round of iteration as an initial reconstructed image in the next round of iteration. The reconstruction algorithm of the present invention does not depend on a conformity degree between anatomical structure information and functional information, and can distinguish image edges well regardless of whether there is noise interfering with the image edges.
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