Abstract: The present disclosure provides a system for image reconstruction. The system may obtain an initial image of a subject. The initial image may be generated based on scan data of the subject that is collected by an imaging device. The system may also generate a gradient image associated with the initial image. The system may further generate a target image of the subject by applying an image reconstruction model based on the initial image and the gradient image. The target image may have a higher image quality than the initial image.

Abstract: A system and a method for medical image processing are provided. The method includes: obtaining a first medical image; obtaining a trained image perception restoration model; the trained image perception restoration model includes an image quality perception sub-model and an image restoration sub-model; and inputting the first medical image into the trained image perception restoration model to obtain a second medical image. The image quality perception sub-model is configured to determine either or both of a first quality evaluation value of the first medical image and a second quality evaluation value of the second medical image, the image restoration sub-model is configured to determine the second medical image, and the quality of the second medical image is higher than that of the first medical image.

Abstract: The present disclosure relates to systems and methods for image processing. The systems may acquire imaging data. The systems may generate multiple intermediate images based on the imaging data by performing image optimization of multiple image optimization dimensions using a plurality of trained machine learning models. The systems may generate a target image based on the intermediate images.

Abstract: The present disclosure relates to systems and methods for image processing. The systems may acquire imaging data. The systems may generate multiple intermediate images based on the imaging data by performing image optimization of multiple image optimization dimensions using a plurality of trained machine learning models. The systems may generate a target image based on the intermediate images.

Abstract: The present disclosure provides a system and method for image reconstruction. The method may include obtaining training samples, the training samples including at least one sample first image generated based on a first tracer, at least one reference first image each of which corresponds to one of the at least one sample first image and has a higher image quality than the corresponding sample first image, at least one sample second image generated based on a second tracer different from the first tracer, and at least one reference second image each of which corresponds to one of the at least one sample second images and has a higher image quality than the corresponding sample second image; and generating a trained image processing model by training a preliminary model using the training samples.

Abstract: An alert apparatus starts alerting a driver of a host vehicle, when an oncoming vehicle, that is an other vehicle traveling in an oncoming lane with respect to a traveling lane in which the host vehicle is traveling so as to approach the host vehicle and that is predicted to pass through on a specific direction side with respect to a current position of the host vehicle, is present, a turn signal indicator of the host vehicle corresponding to the specific direction side is being operated, and it is likely that the host vehicle starts turning to the specific direction side.

Abstract: The present disclosure provides a system and method for image reconstruction. The method may include obtaining training samples, the training samples including at least one sample first image generated based on a first tracer, at least one reference first image each of which corresponds to one of the at least one sample first image and has a higher image quality than the corresponding sample first image, at least one sample second image generated based on a second tracer different from the first tracer, and at least one reference second image each of which corresponds to one of the at least one sample second images and has a higher image quality than the corresponding sample second image; and generating a trained image processing model by training a preliminary model using the training samples.

Abstract: In a driving assist apparatus which carries out a collision avoidance assist that is an assist for avoiding a collision of a self-vehicle and a collision risk object that is an object judged to have a high possibility to collide with the self-vehicle when the collision risk object is judged to exist, an object which does not exist in an object detection range of a front side radar and whose predicted running locus and a predicted running locus of a self-vehicle intersect at a position within a predetermined distance from the self-vehicle on the front side of the self-vehicle is selected as a target of judgment of a possibility to collide with the self-vehicle based on information acquired by the front lateral side radar, among the objects detected by the front lateral side radar.

Abstract: Embodiments of the present disclosure provide a medical image processing method, system, and storage medium. The method includes determining a plurality of images of regions of interest (ROIs) based on a first image of a subject; determining a plurality of second images based on the plurality of images of the ROIs and image processing models corresponding to the plurality of images of the ROIs; and performing a fusion operation based on the plurality of second images to obtain a target image.

Abstract: Provided are a system and a method for positron emission computed tomography (PET) image reconstruction. The method may be implemented on a computing device having at least one processor and at least one storage device, comprising: determining correction data based on original PET data (210); determining reconstruction data to be reconstructed based on the correction data (220); and generating, based on the reconstruction data, one or more of a PET reconstruction image and a PET parametric image (230).

Abstract: An alert apparatus starts alerting a driver of a host vehicle, when an oncoming vehicle, that is an other vehicle traveling in an oncoming lane with respect to a traveling lane in which the host vehicle is traveling so as to approach the host vehicle and that is predicted to pass through on a specific direction side with respect to a current position of the host vehicle, is present, a turn signal indicator of the host vehicle corresponding to the specific direction side is being operated, and it is likely that the host vehicle starts turning to the specific direction side.

Abstract: A vehicle control apparatus selects, as a first group, objects in a predetermined area from objects included in object information, selects, as a second group, an upper limit number of the objects from the first group in descending order of a priority value when the number of the objects of the first group is greater than the upper limit number, and executes a collision avoidance control when an index value satisfies a predetermined condition. The priority value represents a probability that the own vehicle collides with the object. The apparatus reduces the priority value of a particular object of the first group when the moving speed of the own vehicle is equal to or lower than a moving speed threshold. The particular object is an object which is deemed to have a moving speed lower than a moving speed of a four-wheel vehicle.

Abstract: An alert apparatus starts alerting a driver of a host vehicle, when an oncoming vehicle, that is an other vehicle traveling in an oncoming lane with respect to a traveling lane in which the host vehicle is traveling so as to approach the host vehicle and that is predicted to pass through on a specific direction side with respect to a current position of the host vehicle, is present, a turn signal indicator of the host vehicle corresponding to the specific direction side is being operated, and it is likely that the host vehicle starts turning to the specific direction side.

Abstract: System for image correction in PET is provided. The system may acquire a PET image and a CT image of a subject. The system may generate, based on the PET image and the CT image, an attenuation-corrected PET image of the subject by application of an attenuation correction model. The attenuation correction model may be a trained cascaded neural network including a trained first model and at least one trained second model downstream to the trained first model. During the application of the attenuation correction model, an input of each of the at least one trained second model may include the PET image, the CT image, and an output image of a previous trained model that is upstream and connected to the trained second model.

Abstract: In a driving assist apparatus which carries out a collision avoidance assist that is an assist for avoiding a collision of a self-vehicle and a collision risk object that is an object judged to have a high possibility to collide with the self-vehicle when the collision risk object is judged to exist, an object which does not exist in an object detection range of a front side radar and whose predicted running locus and a predicted running locus of a self-vehicle intersect at a position within a predetermined distance from the self-vehicle on the front side of the self-vehicle is selected as a target of judgment of a possibility to collide with the self-vehicle based on information acquired by the front lateral side radar, among the objects detected by the front lateral side radar.

Abstract: The present disclosure provides a system for image reconstruction. The system may obtain an initial image of a subject. The initial image may be generated based on scan data of the subject that is collected by an imaging device. The system may also generate a gradient image associated with the initial image. The system may further generate a target image of the subject by applying an image reconstruction model based on the initial image and the gradient image. The target image may have a higher image quality than the initial image.

Abstract: An alert apparatus sets a determination area which is at a turning direction side of an own vehicle with respect to a longitudinal direction line of the own vehicle and extends forward from the own vehicle or from near the own vehicle when an angle condition that a moving direction angle corresponding to an angle defined by a longitudinal direction line of the own vehicle and a moving direction of an approaching vehicle is within a predetermined angle range, is satisfied. The alert apparatus sets the determination area which is at the turning direction side of the longitudinal direction line and away forward from the own vehicle by a predetermined distance when the angle condition is not satisfied.

Abstract: System for image correction in PET is provided. The system may acquire a PET image and a CT image of a subject. The system may generate, based on the PET image and the CT image, an attenuation-corrected PET image of the subject by application of an attenuation correction model. The attenuation correction model may be a trained cascaded neural network including a trained first model and at least one trained second model downstream to the trained first model. During the application of the attenuation correction model, an input of each of the at least one trained second model may include the PET image, the CT image, and an output image of a previous trained model that is upstream and connected to the trained second model.

Abstract: System for image correction in PET is provided. The system may acquire a PET image and a CT image of a subject. The system may generate, based on the PET image and the CT image, an attenuation-corrected PET image of the subject by application of an attenuation correction model. The attenuation correction model may be a trained cascaded neural network including a trained first model and at least one trained second model downstream to the trained first model. During the application of the attenuation correction model, an input of each of the at least one trained second model may include the PET image, the CT image, and an output image of a previous trained model that is upstream and connected to the trained second model.

Abstract: The present disclosure provides a system for image reconstruction. The system may obtain an initial image of a subject. The initial image may be generated based on scan data of the subject that is collected by an imaging device. The system may also generate a gradient image associated with the initial image. The system may further generate a target image of the subject by applying an image reconstruction model based on the initial image and the gradient image. The target image may have a higher image quality than the initial image.