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: Ultrasound image devices, systems, and methods are provided. An ultrasound imaging system, comprising an array of acoustic elements configured to transmit ultrasound energy into an anatomy in accordance with a first preset acquisition setting, and to receive ultrasound echoes associated with the anatomy; and a processor circuit in communication with the array of acoustic elements and configured to receive, from the array, ultrasound channel data corresponding to the received ultrasound echoes; generate a first set of beamformed data by applying a predictive network to the ultrasound channel data, wherein the first set of beamformed data is associated with a second preset acquisition setting different than the first preset acquisition setting; generate an image of the anatomy from the first set of beamformed data; and output, to a display in communication with the processor circuit, the image of the anatomy.

Abstract: Apparatuses, systems, and methods are provided for automatically selecting first and last frames for a sequence of frames from which an accumulation contrast image may be generated. In some examples, statistical distributions of groups of pixels of the image frames may be analyzed to generate parametric maps. The parametric maps may be analyzed to select the first and last image frames of the sequence. In some examples, an image frame corresponding to the parametric map having a value above a threshold value may be selected as a first frame. In some examples, an image frame corresponding to the parametric map having a maximum value of all the parametric maps may be selected as the last frame. In some examples, the parametric maps may be used to segment features, such as a tumor, from the image frame.

Abstract: A method predicts an interpretation time for a medical image examination of a subject comprising one or more medical images. A plurality of data inputs is obtained, where the data inputs are associated with the medical image examination or the subject of the medical image examination, and the data points represent parameters affecting the interpretation time. The plurality of data inputs are input to a trained artificial intelligence algorithm, wherein the algorithm automatically provides a predicted interpretation time based on said plurality of data inputs. The predicted interpretation time is output to a clinical management system. A clinical management system incorporating the aforementioned method and a computer program product encoded with the aforementioned method are also provided.

Abstract: A method and system (100) for augmented interpretation of shear wave elastography between first and second imaging modalities comprises performing an elastography measurement via a second imaging modality (20), different from a first imaging modality (10), to obtain at least one second imaging modality elastography value (32, 60) of a region of interest (33). At least one corresponding first imaging modality elastography value (36, 38, 62) is predicted based on the obtained second imaging modality elastography value. A graphical user interface or smart report dashboard (50) is generated that shows (i) a fibrosis level (521) of the region of interest, wherein the fibrosis level is determined as a function of (i)(a) the at least one second imaging modality elastography value (32) and/or (i)(b) the predicted at least one corresponding first imaging modality elastography value (36, 38).

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: An ultrasound imaging system may analyze motion of landmarks in a temporal sequence of image frames to determine mobility of one or more landmarks. In some examples, the sequence of image frames may be analyzed by one or more artificial intelligence models to detect landmarks and generate flow fields for the detected landmarks. The flow fields may be analyzed to determine the isotropy of the movement of the landmarks. The isotropy analysis may be used to generate a quantitative mobility index.

Abstract: Systems and methods for ultrasound image acquisition, tracking and review are disclosed. The systems can include an ultrasound probe coupled with at least one tracking device configured to determine a position of the probe based on a combination of ultrasound image data and probe orientation data. The image data can be used to determine a physical reference point and superior-inferior probe coordinates within a patient being imaged, which can be supplemented with the probe orientation data to determine lateral coordinates of the probe. A graphical user interface can display imaging zones corresponding to a scan protocol, along with an imaging status of each zone based at least in part on the probe position. Ultrasound images acquired by the systems can be tagged with spatial indicators and severity indicators, after which the images can be stored for later retrieval and expert review.

Abstract: An ultrasound imaging system includes an array of acoustic elements and a processor circuit configured for communication with the array of acoustic elements. The processor circuit may be configured to control the array of acoustic elements to transmit first ultrasound energy at a first frequency and receive echoes associated with the first ultrasound energy. The processor circuit may be configured to identify an acoustic window based on the echoes associated with the first ultrasound energy and determine a second frequency based on the acoustic window. The processor circuit may be configured to control the array of acoustic elements to transmit second ultrasound energy at the second frequency and receive echoes associated with the second ultrasound energy. The processor circuit may be configured to generate an image based on the echoes associated with the second ultrasound energy and to output the image to a display in communication with the processor circuit.

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: A system for obtaining medical ultrasound images of a subject comprises a probe comprising an ultrasound transducer for capturing an ultrasound image, a memory comprising instruction data representing a set of instructions, and a processor configured to communicate with the memory and to execute the set of instructions. The set of instructions, when executed by the processor, cause the processor to receive an ultrasound image taken by the probe, provide the image as input to a trained model, receive from the model an indication of whether the image comprises an image relevant to a medical diagnostic process, and determine whether to bookmark the image, based on the received indication.

Abstract: An ultrasound imaging system may determine whether a proper cardiac view has been acquired. When a proper cardiac view has been acquired, the ultrasound imaging system may adjust the imaging parameters to visualize epicardial adipose tissue (EAT). Once an image with the adjusted imaging parameters is acquired, the ultrasound imaging system may segment the EAT from the image. Measurements of the EAT may be acquired from the segmented image. In some examples, a report on whether a patient requires follow-up may be generated based on the EAT measurements and patient information.

Abstract: Systems and methods for multi-level vascular imaging for construction and display of vasculature from large to small vessels and micro-vessels using a combination of varying resolution contrast enhanced ultrasound flow imaging modalities are disclosed. While one or more resolution flow imaging modes may be employed for imaging large to small vessels of a vascular tree within a large region of interest, a high resolution mode, such as super resolution imaging, constructed for delineation of the microvascular morphology and directional microcirculation is provided within one or more small ROIs placed in selected locations within the larger ROI.

Abstract: Systems and methods for generating adaptive contrast accumulation imaging images are disclosed. A point spread function thinning/skeletonization technique may be performed on contrast enhanced image frames. An aggressiveness parameter of the technique may be adapted temporally and/or spatially. The aggressiveness parameter may be adapted based on various factors, including, but not limited to, time since injection of the contrast agent, signal intensity, and/or vessel size. The images may be temporally accumulated to generate a final sequence of adaptive contrast accumulation imaging images.

Abstract: Ultrasound image devices, systems, and methods are provided. An ultrasound imaging system comprising a processor circuit in communication with an ultrasound probe comprising a transducer array, wherein the processor circuit is configured to receive, from the ultrasound probe, a first image of a patients anatomy; detect, from the first image, a first anatomical landmark at a first location along a scanning trajectory of the patients anatomy; determine, based on the first anatomical landmark, a steering configuration for steering the ultrasound probe towards a second anatomical landmark at a second location along the scanning trajectory; and output, to a display in communication with the processor circuit, an instruction based on the steering configuration to steer the ultrasound probe towards the second anatomical landmark at the second location.

Abstract: Systems and methods for time-varying two-dimensional (2D) color maps for visualizing images or sequences of images are disclosed. The 2D color map may include brightness values of a hue corresponding to different intensities that changes over time. In some 2D color maps, the hue used for the intensity scale may change over time while the brightness of a pixel may not. In some 2D color maps, both the hue and the brightness may change relative to intensity over time.

Abstract: A system (100) and method (2000): specify image features (1342/1344/1346) which are to be avoided in selecting a region of interest (10) in tissue of a body for making a shear wave elasticity measurement of the tissue; receive (2020) one or more image signals from an acoustic probe (120) produced from acoustic echoes from an area of the tissue; process (2030) acoustic images (504/506/508) in real-time to identify the image features which are to be avoided in selecting the region of interest; provide (2040) visual feedback to a user to choose a location for the region of interest based on the identified image features; select (2050) the region of interest in response to the visual feedback; and make (2060) the shear wave elasticity measurement of the tissue within the selected region of interest using one or more acoustic radiation force pulses.

Abstract: For each of a plurality of time frames in the scan session for producing acoustic images of an area of interest, including a cervix, a system and method: construct (1520) a three dimensional volume of the area of interest from one or more image signals and the inertial measurement signal; apply (1530) a deep learning algorithm to the constructed three dimensional volume of interest to qualify an image plane for obtaining a candidate cervical length for the cervix; perform (1540) image segmentation and object detection for the qualified image plane to obtain the candidate cervical length. The shortest candidate cervical length from the plurality of time frames is selected as the measured cervical length for the scan session. A display device (116) displays an image of the cervix corresponding to the measured cervical length for the scan session, and an indication of the measured cervical length for the scan session.

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.