Abstract: Apparatuses, systems, and techniques to perform registration among images. In at least one embodiment, one or more neural networks are trained to indicate registration of features in common among at least two images by generating a first correspondence by simulating a registration process of registering an image and applying the at least two images and the first correspondence to a neural network to derive a second correspondence of the features in common among the at least two images.

Abstract: Apparatuses, systems, and techniques to perform registration among images. In at least one embodiment, one or more neural networks are trained to indicate registration of features in common among at least two images by generating a first correspondence by simulating a registration process of registering an image and applying the at least two images and the first correspondence to a neural network to derive a second correspondence of the features in common among the at least two images.

Abstract: Disclosed in embodiments of the present disclosure are a driver chip and a display device. The driver chip includes two opposite long edges, and two opposite short edges connected to the long edges; the driver chip includes multiple output pins and multiple input pins; the output pins are close to one of the long edges; the input pins include first input pins, second input pins, and third input pins; the first input pins are close to the long edge opposite to the output pins; the second input pins are close to the short edges; the third input pins are close to the long edge and located on both sides of the output pins.

Abstract: A micro-robot magnetic drive device and a control method based on double closed loop three-dimensional path tracking are disclosed. The method includes: inputting a desired tracking path, obtaining current pose information of a magnetic micro-robot through a camera, and then calculating a position of a center of mass, an actual axial direction, coordinates of a desired position point with the shortest distance from the center of mass on a desired tracking path, and a tangent direction of this point; calculating a horizontal distance, a vertical distance, a direction angle error, and a pitch angle error of the two points according to the actual axial direction, the tangent direction, and disturbance compensation; and obtaining a required rotating magnetic field according to a designed position closed loop controller.

Abstract: Disclosed is a CT image generation method for attenuation correction of PET images. According to the method, a CT image and a PET image at T1 and a PET image at T2 are acquired and input into a trained deep learning network to obtain a CT image at T2; the CT image can be applied to the attenuation correction of the PET image, thereby obtaining more an accurate PET AC (Attenuation Correction) image. According to the CT image generation method for attenuation correction of PET images, the dosage of X-rays received by a patient in the whole image acquisition stage can be reduced, and physiological and psychological pressure of the patient is relieved. In addition, the later image acquisition only needs a PET imaging device, without the need of PET/CT device, cost of imaging resource distribution can be reduced, and the imaging expense of the whole stage is reduced.

Abstract: A method for imaging may include directing a PET scanner to perform scans of a subject injected with a tracer during a first time period. The method may also include generating PET images by reconstructing the PET data that are generated by the PET scanner based on the scans of the subject. The method may also include determining a first portion of a blood input function of the tracer in the subject based on the PET images. The method may also include determining an integral of a second portion of the blood input function based on a kinetic model, the PET data, and the first portion of the blood input function. The method may also include determining kinetic parameters of the kinetic model based on the PET data, the first portion of the blood input function, and the integral of the second portion of the blood input function.

Abstract: Provided are a driving method, a gate drive unit and a display touch device. The driving method is applied to the gate drive unit in the display touch device. The gate drive unit includes at least one pull-down circuit. The pull-down circuit is coupled with a pull-up node and a pull-down voltage terminal, and is configured to control connection or disconnection between the pull-up node and the pull-down voltage terminal; one frame of image display time includes a display phase and a touch phase that are alternately set, and at least one of the touch phase is set between two adjacent display phases. The driving method includes: during the touch phase, controlling, by the at least one pull-down circuit, the disconnection between the pull-up node and the pull-down voltage terminal. The problem of a screen flashing phenomenon of existing display touch devices during the touch phase is solved.

Abstract: Disclosed is a method for multi-center effect compensation based on a PET/CT intelligent diagnosis system. The method includes the following steps: estimating multi-center effect parameters of a test center B relative to a training center A by implementing a nonparametric mathematical method for data of the training center A and the test center B based on a location-scale model about additive and multiplicative multi-center effect parameters, and using the parameters to compensate the data of the test center B to eliminate a multi-center effect between the test center B and the training center A. According to the present disclosure, the multi-center effect between the training center A and the test center B can be compensated, so that the compensated data of the test center B can be used in the model trained by the training center A, and the generalization ability of the model is indirectly improved.

Abstract: Disclosed is an automatic reorientation method from an SPECT three-dimensional reconstructed image to a standard view, wherein a rigid registration parameter P between a SPECT three-dimensional reconstructed image A and a standard SPECT image R is extracted by using a rigid registration algorithm to form a mapping database of A and P; features of the image A are extracted by using a three-layer convolution module, and are converted into a 6-dimensional feature vector T after three times of full connection, and T is applied to A through a spatial transformer network to form an orientation result predicted by the network, thus establishing the automatic reorientation model of the SPECT three-dimensional reconstructed image. The SPECT three-dimensional reconstructed image to be orientated is taken as an input. A standard view can be obtained by using the automatic reorientation model of the SPECT three-dimensional reconstructed image for automatic turning.

Abstract: Disclosed is a method for region-of-interest enhanced PET image reconstruction based on multi-task learning, which comprises the following steps: firstly, acquiring a backprojection image of the PET original data, and designing a main task of establishing a mapping between the backprojection image and a reconstructed PET image by using a three-dimensional deep convolution neural network. A new auxiliary task 1 is designed to predict a computerized tomography (CT) image with the same anatomical structures as the PET image reconstructed from the backprojection image, so as to reduce the noise in the reconstructed PET image by using the local smoothing information of the high-resolution CT image.

Abstract: Disclosed is a CT image generation method for attenuation correction of PET images. According to the method, a CT image and a PET image at T1 and a PET image at T2 are acquired and input into a trained deep learning network to obtain a CT image at T2; the CT image can be applied to the attenuation correction of the PET image, thereby obtaining more an accurate PET AC (Attenuation Correction) image. According to the CT image generation method for attenuation correction of PET images, the dosage of X-rays received by a patient in the whole image acquisition stage can be reduced, and physiological and psychological pressure of the patient is relieved. In addition, the later image acquisition only needs a PET imaging device, without the need of PET/CT device, cost of imaging resource distribution can be reduced, and the imaging expense of the whole stage is reduced.

Abstract: Disclosed is a diagnostic apparatus for a chronic obstructive pulmonary disease (COPD) based on prior knowledge CT subregion radiomics, belonging to the field of medical imaging. The diagnostic apparatus comprises: a subregion partitioning module based on prior knowledge configured for partitioning a CT lung image of a patient into three subregions based on the CT values of the interior of the lung, wherein the CT value of the interior of the lung of a subregion 1 is in the range of (?1024, ?950), the CT value of the interior of the lung of a subregion 2 is in the range of (?190, 110), and the CT value of the interior of the lung of a subregion 3 is in the range of (?950, ?190); a feature extraction module configured for extracting the radiomics features of the three subregions, respectively, and obtaining the LAA-950I features.

Abstract: Disclosed is a method for automatic classification of pathological images based on a staining intensity matrix. This method directly extracts the staining intensity matrix irrelevant to a stain ratio, a staining platform, a scanning platform and some human factors in the pathological image as the feature information of classification, without restoring normalized stained images, while retaining all impurity-free information related to diagnosis. It avoids the phenomenon that the diagnostic effect of the existing computer-aided diagnosis method of pathological images based on the traditional color normalization method changes with the changes of the selected standard pathological sections. Moreover, it avoids the error introduced by the need to restore the stained image, and has a higher diagnostic accuracy and a more stable diagnostic effect. At the same time, the method can realize the diagnosis of pathological images in a shorter time, which is easy to realize and more practical.

Abstract: The present disclosure provides a system and method for PET image reconstruction. The method may include processes for obtaining physiological information and/or rigid motion information. The image reconstruction may be performed based on the physiological information and/or rigid motion information.

Abstract: Disclosed is a method for multi-center effect compensation based on a PET/CT intelligent diagnosis system. The method includes the following steps: estimating multi-center effect parameters of a test center B relative to a training center A by implementing a nonparametric mathematical method for data of the training center A and the test center B based on a location-scale model about additive and multiplicative multi-center effect parameters, and using the parameters to compensate the data of the test center B to eliminate a multi-center effect between the test center B and the training center A. According to the present disclosure, the multi-center effect between the training center A and the test center B can be compensated, so that the compensated data of the test center B can be used in the model trained by the training center A, and the generalization ability of the model is indirectly improved.

Abstract: Disclosed is a three-dimensional low-dose PET reconstruction method based on deep learning. The method comprises the following steps: back projecting low-dose PET raw data to the image domain to maintain enough information from the raw data; selecting an appropriate three-dimensional deep neural network structure to fit the mapping between the back projection of the low-dose PET and a standard-dose PET image; after learning from the training samples the network parameters are fixed, realizing three-dimensional PET image reconstruction starting from low-dose PET raw data, thereby obtaining a low-dose PET reconstructed image which has a lower noise and a higher resolution compared with the traditional reconstruction algorithm and image domain noise reduction processing.

Abstract: Disclosed in embodiments of the present disclosure are a driver chip and a display device. The driver chip includes two opposite long edges, and two opposite short edges connected to the long edges; the driver chip includes multiple output pins and multiple input pins; the output pins are close to one of the long edges; the input pins include first input pins, second input pins, and third input pins; the first input pins are close to the long edge opposite to the output pins; the second input pins are close to the short edges; the third input pins are close to the long edge and located on both sides of the output pins.

Abstract: Provided are a driving method, a gate drive unit and a display touch device. The driving method is applied to the gate drive unit in the display touch device. The gate drive unit includes at least one pull-down circuit. The pull-down circuit is coupled with a pull-up node and a pull-down voltage terminal, and is configured to control connection or disconnection between the pull-up node and the pull-down voltage terminal; one frame of image display time includes a display phase and a touch phase that are alternately set, and at least one of the touch phase is set between two adjacent display phases. The driving method includes: during the touch phase, controlling, by the at least one pull-down circuit, the disconnection between the pull-up node and the pull-down voltage terminal. The problem of a screen flashing phenomenon of existing display touch devices during the touch phase is solved.

Abstract: Disclosed is an automatic reorientation method from an SPECT three-dimensional reconstructed image to a standard view, wherein a rigid registration parameter P between a SPECT three-dimensional reconstructed image A and a standard SPECT image R is extracted by using a rigid registration algorithm to form a mapping database of A and P; features of the image A are extracted by using a three-layer convolution module, and are converted into a 6-dimensional feature vector T after three times of full connection, and T is applied to A through a spatial transformer network to form an orientation result predicted by the network, thus establishing the automatic reorientation model of the SPECT three-dimensional reconstructed image. The SPECT three-dimensional reconstructed image to be orientated is taken as an input. A standard view can be obtained by using the automatic reorientation model of the SPECT three-dimensional reconstructed image for automatic turning.

Abstract: Methods, apparatuses, devices and computer-readable storage media for action transfer are provided. In one aspect, a method includes: obtaining an initial video involving an action sequence of an initial object, identifying a two-dimensional skeleton keypoint sequence of the initial object from plural frames of image in the initial video, converting the two-dimensional skeleton keypoint sequence of the initial object into a three-dimensional skeleton keypoint sequence of a target object, and generating a target video involving an action sequence of the target object based on the three-dimensional skeleton keypoint sequence of the target object.