Shenzhen Institutes of Advanced Technology

Chinese Academy of Sciences

Huawei Technologies Co., Limited

Shanghai United Imaging Healthcare Co., Ltd.

Infinitus (china) Company Ltd.

Shenzhen W & F Sales Co., Ltd.

Shenzhen Supervisions Technology Co., Ltd.

Dong Liang

Xin Liu

Hairong Zheng

Zhanli Hu

Yongfeng Yang

Na Zhang

Shanshan Wang

Haifeng Wang

Sheng Liu

Yong Yang

Xi Peng

Zhan Li Hu

Hai Rong Zheng

Yong Li

Shimon Shilo

Jun Hui Liu

Doron Ezri

Qiang Zhang

Jian Tang

Guobin Li

Yuanyuan Liu

Qiang He

Yong Chao Zhou

Xiaohui Wang

Qi Yang

Junping Zhang

Chao Zou

Chirn Chye Boon

Zheng Xiao

Sen Jia

Zehua Chen

Xiang Yi

Shi Su

Dror Regev

Gengen Hu

Zhen Xing Huang

Sha Tan

Qiu Zhang

Hanyu Sun

Yonghua Wang

Taohui Xiao

Yanjie Zhu

Zhiyuan Huang

Qingneng Li

Chenfeng Jing

Dongfang Gao

Zhilang Qiu

Yingjie Xu

Ningbo Huang

San Wu

Zhonghua Kuang

Jing Cheng

Chunhui Zhang

Qihang Chen

A low-dose PET image restoration method and system, a device, and a medium are provided. The method includes: S1, performing blocking processing on a training image comprising a low-dose PET image, an MR image, and a standard-dose PET image to obtain a first patch, and performing first preprocessing on the first patch to obtain a second patch; S2, obtaining, according to the second patch, a first joint dictionary by means of sparse coding and dictionary updating; and S3, restoring the low-dose PET image to a restored image of the standard-dose PET image according to the first joint dictionary.

Low-dose PET image restoration method and system, device, and medium

A medical image noise reduction method, system, terminal and storage medium are disclosed. The method includes: obtaining a standard-dose PET image and a constant-value image; inputting the standard-dose PET image and the constant-value image into a decay function to obtain a corresponding low-dose noisy PET image and a noisy constant-value image, respectively; assembling the low-dose noisy PET image and the noisy constant-value image in a width dimension or a height dimension, and then inputting into a trained conjugate generative adversarial network, and outputting a denoised PET image and constant-value image output by the conjugate generative adversarial network.

Medical image noise reduction method, system, terminal, and storage medium

A medical image processing method and processing apparatus, and a computer readable storage medium. The method includes: obtaining a to-be-processed image; performing a feature extraction on the to-be-processed image to obtain a corresponding feature image; and re-determining a pixel value of each pixel in the to-be-processed image based on first information and second information of a corresponding pixel in the feature image, and processing the to-be-processed image; wherein the first information is information of a pixel adjacent to the corresponding pixel in the features image, and the second information is information of a pixel that is not adjacent to and is similar to the corresponding pixel in the features image.

Medical image processing method, processing apparatus, and computer-readable storage medium

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.

Methods and apparatuses for training magnetic resonance imaging model

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.

Method and terminal device for processing Positron Emission Tomography data

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.

Method, device and equipment for reconstructing PET images

A magnetic resonance imaging method includes: obtaining three-dimensional under-sampling data of a target object based on a first three-dimensional magnetic resonance imaging sequence; obtaining a three-dimensional point spread function based on the three-dimensional under-sampling data or a two-dimensional mapping data of the target object; obtaining a sensitivity map of the target object based on the data collected by three-dimensional low-resolution complete sampling; performing imaging reconstruction to the three-dimensional under-sampling data based on the three-dimensional point spread function and the sensitivity map to obtain a reconstructed magnetic resonance image. The first three-dimensional magnetic resonance imaging sequence has a first sinusoidal gradient field on a phase direction and a second sinusoidal gradient field on a layer selection direction. 0-order moments of the first and the second three-dimensional magnetic resonance imaging sequences are 0. A phase difference between the first and the second three-dimensional magnetic resonance imaging sequence is π/2.

Magnetic resonance imaging method, apparatus, and computer storage medium

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.

Apriori guidance network for multitask medical image synthesis

Provided are a method and device for magnetic resonance parameter imaging, medical equipment, and a storage medium. The method comprises: performing acceleration sampling with respect to an image to be reconstructed of an observation target in a preset parameter direction to acquire K-space data corresponding to the image to be reconstructed, calculating, on the basis of the K-space data and of a parameter relaxation model, a parameter value and a compensation coefficient of the image to be reconstructed, generating, on the basis of the compensation coefficient, a compensation image corresponding to the image to be reconstructed, calculating, on the basis of the compensation image, respectively a low-rank part and a sparse part of the image to be reconstructed so as to update the compensation image.

Magnetic resonance imaging method, device, medical device and storage medium

A method for magnetic resonance imaging and plaque recognition includes: obtaining magnetic resonance undersampled K-space data; transforming the magnetic resonance undersampled K-space data to an image domain through inverse Fourier transform to obtain a preprocessed image; reconstructing the preprocessed image through a pre-established deep learning reconstruction model to obtain a high-resolution imaging image of a blood vessel wall; and recognizing plaques in the high-resolution imaging image of the blood vessel wall through a pre-established deep learning plaque recognition model. A neural network corresponding to the pre-established deep learning reconstruction model is a dense connection network. The magnetic resonance undersampled K-space data is head-and-neck combined magnetic resonance undersampled K-space data of the blood vessel wall.

Method and apparatus for magnetic resonance imaging and plaque recognition

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 functionfor improving image quality; and training the first multi-task learning-type generative adversarial network according to the joint loss functionin combination with an optimizer to provide a second multi-task learning-type generative adversarial network.

Method and system for generating multi-task learning-type generative adversarial network for low-dose PET reconstruction

A single-shot Fresnel non-coherent correlation digital holographic device based on a polarization-oriented planar lens, comprising: A polarization-oriented planar lens () for wavefront modulation and beam splitting, a focusing element (), a half-wave plate () with a small hole and a polarization imaging camera (). Incident light passes through the polarization-oriented planar lens () and the focusing element () and is divided into two beams with different polarizations, that is, focused and parallel or focused and divergent beams, wherein the focused beam passes through the small hole of the half-wave plate (), the parallel or divergent beam passes through the half-wave plate (), so as to make the polarization of the two beams consistent behind pass through the half-wave plate ().

Single-shot Fresnel non-coherent correlation digital holographic device based on polarization-oriented planar lens

Disclosed are information transmission method, apparatus, device and medium for medical imaging application. The method includes: an imaging device corresponding to an application authorization request is determined according to the received application authorization request, and an application permission profile of the imaging device is acquired; and a medical imaging application corresponding to the imaging device is determined according to the application permission profile of the imaging device, and medical imaging application information corresponding to the medical imaging application is transmitted to the imaging device.

Information transmission method, apparatus, device and medium for medical imaging application

Provided are a parallel magnetic resonance imaging method and apparatus based on adaptive joint sparse codes and a computer-readable medium. The method includes solving an l&#x2212;l&#x2212;lminimization objective, where the lnorm is a data fitting term, the lnorm is a sparse representation error, and the lmixed norm is the joint sparsity constraining across multiple channels; separately updating each of a sparse matrix, a dictionary and K-space data with a corresponding algorithm, and obtaining a reconstructed image by a sum of root mean squares of all the channels. The joint sparsity of the channels is developed using the norm l. In this manner, calibration is not required while information sparsity is developed. Moreover, the method is robust.

Adaptive joint sparse coding-based parallel magnetic resonance imaging method and apparatus and computer readable medium

There are provided a parallel rapid imaging method and device based on a complex number conjugate symmetry of multi-channel coil data and nonlinear GRAPPA image reconstruction, and a medium. The imaging method includes: obtaining virtual conjugate coil data by expanding the actual multi-channel coil data; combining actual multi-channel coil data and virtual multi-channel coil data to obtain a linear data term and a nonlinear data term; calibrating weighting factors of the linear data term and the nonlinear data term by using combined low-frequency full-sampling data (margins of the low-frequency full-sampling data includes parts of high-frequency data); reconstructing data which is under-sampled in a high-frequency region according to the calibrated weighting factors; fusing the low-frequency full-sampling data and the reconstructed data for the high-frequency region.

Imaging method and device for nonlinear parallel magnetic resonance image reconstruction, and medium

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Guangdong, China

Dong Liang