Abstract: Methods and systems are provided for generating user guidance for ultrasound imaging. In one example, a method includes determining, with a probe recommendation model, a user action to an ultrasound probe prior to and/or during acquisition of a current ultrasound image frame, one or more anatomical features in the current ultrasound image frame, and an anatomy view of the current ultrasound image frame, and outputting, for display on a display device, a probe motion recommendation based on the user action, the one or more anatomical features, and the anatomy view.

Abstract: Methods and systems are provided for inferring thickness and volume of one or more object classes of interest in two-dimensional (2D) medical images, using deep neural networks. In an exemplary embodiment, a thickness of an object class of interest may be inferred by acquiring a 2D medical image, extracting features from the 2D medical image, mapping the features to a segmentation mask for an object class of interest using a first convolutional neural network (CNN), mapping the features to a thickness mask for the object class of interest using a second CNN, wherein the thickness mask indicates a thickness of the object class of interest at each pixel of a plurality of pixels of the 2D medical image; and determining a volume of the object class of interest based on the thickness mask and the segmentation mask.

Abstract: Methods and systems are provided for turbulence monitoring during ultrasound scanning. In one example, during scanning with an ultrasound probe, a turbulence amount between two successive frames may be monitored, and in response to the turbulence amount at or above the higher threshold, deployment of the one or more image interpretation protocols may be stopped or delayed until the turbulence amount decreases below the higher threshold.

Abstract: A method and ultrasound imaging system includes generating a cine including a plurality of cardiac views based on the cardiac ultrasound data, segmenting a plurality of cardiac chambers from each of the plurality of cardiac images, and automatically determining a cardiac chamber area for each of the plurality of cardiac chambers. The method and ultrasound imaging system includes displaying the cine on a display device and displaying a plurality of single trace curves on the display device at the same time as the cine to provide feedback regarding an acquisition quality of the cine, wherein each of the single trace curves represents the cardiac chamber area for a different one of the plurality of cardiac chambers over the plurality of cardiac cycles.

Abstract: Techniques are described for generating mono-modality training image data from multi-modality image data and using the mono-modality training image data to train and develop mono-modality image inferencing models. A method embodiment comprises generating, by a system comprising a processor, a synthetic 2D image from a 3D image of a first capture modality, wherein the synthetic 2D image corresponds to a 2D version of the 3D image in a second capture modality, and wherein the 3D image and the synthetic 2D image depict a same anatomical region of a same patient. The method further comprises transferring, by the system, ground truth data for the 3D image to the synthetic 2D image. In some embodiments, the method further comprises employing the synthetic 2D image to facilitate transfer of the ground truth data to a native 2D image captured of the same anatomical region of the same patient using the second capture modality.

Abstract: Techniques are provided for generating enhanced image representations from original X-ray images using deep learning techniques. In one embodiment, a system is provided that includes a memory storing computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can include a reception component, an analysis component, and an artificial intelligence component. The analysis component analyzes the original X-ray image using an AI-based model with respect to a set of features of interest. The AI component generates a plurality of enhanced image representations. Each enhanced image representation highlights a subset of the features of interest and suppresses remaining features of interest in the set that are external to the subset.

Abstract: Systems and methods for guided lung coverage and automated detection using ultrasound devices are disclosed. The method for guided coverage and automated detection of pathologies of a subject includes positioning an ultrasound probe on a region of the subject body to be imaged. The method includes capturing a video of the subject and processing the video to generate a torso image of the subject and identify location of the ultrasound probe on the subject body. The method includes registering the video to an anatomical atlas to generate a mask of the region of the subject body comprising a plurality of sub-regions of the subject body to be imaged and superimposing the mask over the torso image. The method further includes displaying an indicator corresponding to a location of the each of the plurality of the sub-regions on the torso image.

Abstract: Systems, machine-readable media, and methods for ultrasound imaging can include acquiring three-dimensional data for one or more patient data sets and generating a three-dimensional environment based on one or more transition areas identified between a plurality of volumes of the three-dimensional data. A method can also include generating a set of probe guidance instructions based at least in part on the one or more transition areas and the plurality of volumes of the three-dimensional data, and acquiring, using an ultrasound probe, a first frame of two-dimensional data for a patient. The method can also include executing the set of probe guidance instructions to provide probe feedback for acquiring at least a second frame of two-dimensional data.

Abstract: Systems, methods and computer program products are provided to collect ultrasound (US) data. A processor is configured to acquire the US data along one or more acquisition scan planes. The US data defines a plurality of image frames that have a first image quality. The processor is further configured to apply a generative model to at least one of the US data or plurality of image frames to generate a synthetic scan plane image along a synthetic scan plane. The generative model is defined based on one or more training ultrasound data sets. The synthetic scan plane image has an image quality that is common with the first image quality of the plurality of image frames. The system further comprises a display configured to display the synthetic scan plane image.

Abstract: Methods and systems are provided for turbulence monitoring during ultrasound scanning. In one example, during scanning with an ultrasound probe, a turbulence amount between two successive frames may be monitored, and in response to the turbulence amount at or above the higher threshold, deployment of the one or more image interpretation protocols may be stopped or delayed until the turbulence amount decreases below the higher threshold.

Abstract: A system and method for automatically adjusting beamformer parameters based on ultrasound image analysis to enhance ultrasound image acquisition is provided. The method includes acquiring, by an ultrasound system, an ultrasound image. The method includes segmenting, by at least one processor, the ultrasound image to identify anatomical structure(s) and/or image artifact(s) in the ultrasound image. The method includes detecting, by the at least one processor, a location of each of the identified anatomical structure(s) and/or image artifact(s). The method includes automatically adjusting, by the at least one processor, at least one beamformer parameter based on the detected location of one or more of the identified anatomical structure(s) and/or the image artifact(s). The method includes acquiring, by the ultrasound system, an enhanced ultrasound image based on the automatically adjusted at least one beamformer parameter. The method includes presenting, at a display system, the enhanced ultrasound image.

Abstract: Various methods and systems are provided for generating a context awareness graph for a medical scan image. In one example, the context awareness graph includes relative size and relative position annotations with regard to one or more internal anatomical features in the scan image to enable a user to determine a current scan plane and further, to guide the user to a target scan plane.

Abstract: A method of determining a fraction of one or more phases in a multi-phase fluid in a conduit is provided. The method includes exciting (602) a sensing device to cause emission of electromagnetic waves of a range of frequencies into a multi-phase fluid. The sensing device comprises an antenna and a dielectric layer, selected to cause resonance in at least one of a first set of frequencies or a second set of frequencies based on a flow state of the multi-phase fluid, when placed proximate to the multi-phase fluid. The method also includes receiving (604) transmitted or reflected electromagnetic waves from the multi-phase fluid. The flow state of the multi-phase fluid is selected (606) based on a classification parameter. Fractions are determined (608) utilizing at least one fraction determination model that is selected based on the flow state of the multi-phase fluid.

Abstract: A method of determining a fraction of one or more phases in a multi-phase fluid in a conduit is provided. The method includes exciting (602) a sensing device to cause emission of electromagnetic waves of a range of frequencies into a multi-phase fluid. The sensing device comprises an antenna and a dielectric layer, selected to cause resonance in at least one of a first set of frequencies or a second set of frequencies based on a flow state of the multi-phase fluid, when placed proximate to the multi-phase fluid. The method also includes receiving (604) transmitted or reflected electromagnetic waves from the multi-phase fluid. The flow state of the multi-phase fluid is selected (606) based on a classification parameter. Fractions are determined (608) utilizing at least one fraction determination model that is selected based on the flow state of the multi-phase fluid.