Abstract: Systems and methods for visualizing microbubbles for contrast-enhanced imaging are described herein. Microbubbles may be visualized by multi-pixel spots by identifying and localizing individual microbubbles. Motion compensation may be provided for localized microbubbles or for contrast images prior to microbubble identification and localization. Users may determine the size of the multi-pixel spot by adjusting a filter, for example, adjusting a threshold value of the filter. Altering the size of the multi-pixel spot may alter the resolution. As the number of pixels increases, the resolution decreases, but the acquisition time may also decrease. As the number of pixels decreases, the resolution increases, but the acquisition time may increase.

Abstract: The present disclosure describes ultrasound imaging systems and methods for ultrasonically inspecting biological tissue. An ultrasound imaging system according to the present disclosure may be configured to automatically apply tissue-specific imaging parameter settings (312, 314) based upon the automatic identification of the type of tissue being scanned. Tissue type identification (315) may be performed automatically for the images in the live image stream (304) and thus adjustments to the imaging settings may be applied automatically by using a neural network (320) and thus dynamically during the exam obviating the need for the sonographer to manually switch presets or adjust the imaging settings when moving to different portion of the anatomy.

Abstract: The present disclosure includes ultrasound systems and methods for smart shear wave elastography in anisotropic tissue. An example method may include identifying muscle fiber structures from a 3D ultrasound image dataset. The method may include providing a representation of at least one identified muscle fiber structure relative to a surface of a transducer. The method may include selecting at least one of the identified muscle fiber structures. The method may include determining a target measurement plane based on an orientation of the selected muscle fiber structure. The method may also include transmitting ultrasound pulses in accordance with a sequence configured to perform shear wave imaging in the target measurement plane.

Abstract: Ultrasound image devices, systems, and methods are provided. An ultrasound imaging system comprising a communication interface in communication with an ultrasound imaging device and configured to receive a ultrasound image of a subject's anatomy; and a processor in communication with the communication interface and configured to apply a predictive network to the ultrasound image to identify a plurality of locations of potential malignancies in the subjects anatomy; and determine a plurality of malignancy likelihoods at the plurality of locations of potential malignancies; and output, to a display in communication with the processor, the plurality of locations of potential malignancies and the plurality of malignancy likelihoods for the plurality of locations of potential malignancies to guide a biopsy determination for the subjects anatomy.

Abstract: The present disclosure describes ultrasound imaging systems and methods for ultrasonically inspecting biological tissue, such as liver and for automatically identifying and acquiring a view suitable for hepatic-renal echo-intensity ratio quantification, using one or more neural networks, which may be trained to perform image classification, segmentation, or a combination thereof to compute a confidence metric and to provide a recommendation for measurement ROIs placement on the image.

Abstract: Clinical assessment devices, systems, and methods are provided. A clinical assessment system, comprising a processor in communication with an imaging device, wherein the processor is configured to receive, from the imaging device, a sequence of image frames representative of a contrast agent perfused subjects tissue across a time period; classify the sequence of image frames into a plurality of first tissue classes and a plurality of second tissue classes based on a spatiotemporal correlation among the sequence of image frames by applying a predictive network to the sequence of image frames to produce a probability distribution for the plurality of first tissue classes and the plurality of second tissue classes; and output, to a display in communication with the processor, the probability distribution for the plurality of first tissue classes and the plurality of second tissue classes.

Abstract: Systems and computer implemented method of generating a simulated image of a baby based on an ultrasound image of a fetus. A method comprises acquiring an ultrasound image of the fetus, and using a machine learning model to generate the simulated image of the baby, based on the ultrasound image of the fetus, wherein the simulated image of the baby comprises a prediction of how the fetus will look as a baby.

Abstract: Ultrasound image devices, systems, and methods are provided. A medical ultrasound imaging system, comprising an interface in communication with an ultrasound imaging component and configured to receive a first image representative of blood vessels of a brain of a patient while the ultrasound imaging component is positioned at a first imaging position with respect to the patient; and a processing component in communication with the interface and configured to apply a convolutional network (CNN) to the first image to produce a motion control configuration for repositioning the ultrasound imaging component from the first imaging position to a second imaging position associated with a transcranial examination, the CNN trained based on at least a known blood vessel topography.

Abstract: A method for manufacturing an emulsion comprises drops in an external solution, the drops containing a fluid. The fluid is moved along a first main channel, a portion of the fluid moving in the first main channel is withdrawn via a plurality of microchannels positioned in parallel between the first main channel and a second main channel filled with external solution, the drops are formed by hydrodynamic focusing when the fluid passes from each microchannel into the second main channel.