Systems and methods for performing motion artifact correction in medical images. One method includes receiving, with an electronic processor, a medical image associated with a patient, the medical image including at least one motion artifact. The method also includes applying, with the electronic processor, a model developed using machine learning to the medical image for correcting motion artifacts, the model including at least one of a spatial transformer network and an attention mechanism network. The method also includes generating, with the electronic processor, a new version of the medical image, where the new version of the medical image at least partially corrects the at least one motion artifact.

Systems and methods for performing motion artifact correction in medical images. One method includes receiving, with an electronic processor, a medical image associated with a patient, the medical image including at least one motion artifact. The method also includes applying, with the electronic processor, a model developed using machine learning to the medical image for correcting motion artifacts, the model including at least one of a spatial transformer network and an attention mechanism network. The method also includes generating, with the electronic processor, a new version of the medical image, where the new version of the medical image at least partially corrects the at least one motion artifact.

A deep neural network-based reconstruction system for accelerated magnetic resonance imaging of vessel walls. The system can comprise a magnetic resonance imaging (MRI) scanner configured to obtain an image of the vessel walls, and a computer having a processor. The processor comprises a first and second subnetwork implemented in a cascade fashion. The first subnetwork comprises a convolutional neural network (CNN) and an output correcting module. The first subnetwork receives the image and transforms the image to a reduced artifact image. The second subnetwork is an identical duplicate of the first network. The second subnetwork boosts an accuracy of the reduced artifact image to generate a visual representation of the vessel walls. A computer display terminal is connected to the processor and is configured to display the visual representation of the vessel walls.

A deep neural network-based reconstruction system for accelerated magnetic resonance imaging of vessel walls. The system can comprise a magnetic resonance imaging (MRI) scanner configured to obtain an image of the vessel walls, and a computer having a processor. The processor comprises a first and second subnetwork implemented in a cascade fashion. The first subnetwork comprises a convolutional neural network (CNN) and an output correcting module. The first subnetwork receives the image and transforms the image to a reduced artifact image. The second subnetwork is an identical duplicate of the first network. The second subnetwork boosts an accuracy of the reduced artifact image to generate a visual representation of the vessel walls. A computer display terminal is connected to the processor and is configured to display the visual representation of the vessel walls.

Provided are systems and methods for image processing such as image modification. More particularly, example aspects of the present disclosure are directed to systems and methods for cascaded multi-resolution machine learning for performing image processing on resource-constrained devices.

Provided are systems and methods for image processing such as image modification. More particularly, example aspects of the present disclosure are directed to systems and methods for cascaded multi-resolution machine learning for performing image processing on resource-constrained devices.

Disclosed is a method for assisting a surgeon in bringing a medical implement having a marker to a target location within a body of a patient. According to some embodiments, the method includes receiving a first image of the medical implement on its way to the target location in the body of the patient; processing the image to obtain an enhanced image showing at least said marker; and providing the original image and the enhanced image for display to the surgeon, wherein the enhanced image is provided for display as an inset on a display of the first image.

Disclosed is a method for assisting a surgeon in bringing a medical implement having a marker to a target location within a body of a patient. According to some embodiments, the method includes receiving a first image of the medical implement on its way to the target location in the body of the patient; processing the image to obtain an enhanced image showing at least said marker; and providing the original image and the enhanced image for display to the surgeon, wherein the enhanced image is provided for display as an inset on a display of the first image.

The present disclosure provides an edge extraction method, apparatus, electronic device, and storage medium. The edge extraction method comprises: acquiring a target image to be extracted; and inputting the target image to be extracted to a target edge extraction model to obtain a target edge mask image corresponding to the target image to be extracted. The target edge extraction model is trained by: acquiring a sample initial image to be extracted and a sample initial edge mask image corresponding to the sample initial image to be extracted; performing image enhancement processing on the sample initial image to be extracted to obtain a sample target image to be extracted with a target size, and performing image enhancement processing on the sample initial edge mask image to obtain a sample target edge mask image with the target size; and training an initial deep learning model according to the sample target image to be extracted and the sample target edge mask image corresponding to the sample initial image to be extracted to obtain the target edge extraction model.

The present disclosure provides an edge extraction method, apparatus, electronic device, and storage medium. The edge extraction method comprises: acquiring a target image to be extracted; and inputting the target image to be extracted to a target edge extraction model to obtain a target edge mask image corresponding to the target image to be extracted. The target edge extraction model is trained by: acquiring a sample initial image to be extracted and a sample initial edge mask image corresponding to the sample initial image to be extracted; performing image enhancement processing on the sample initial image to be extracted to obtain a sample target image to be extracted with a target size, and performing image enhancement processing on the sample initial edge mask image to obtain a sample target edge mask image with the target size; and training an initial deep learning model according to the sample target image to be extracted and the sample target edge mask image corresponding to the sample initial image to be extracted to obtain the target edge extraction model.