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: An ultrasound imaging system includes an a processor circuit that stores, in a memory in communication with the processor circuit, a target parameter representative of a target anatomical scan window. The processor circuit receives a first ultrasound image acquired by a first ultrasound probe with a first anatomical scan window during a first acquisition period. The processor circuit determines a first parameter representative of the first anatomical scan window. The processor circuit retrieves the target parameter from the memory. The processor circuit compares the target parameter and the first parameter. The processor circuit outputs a visual representation of the comparison to a display in communication with the processor circuit.

Abstract: The present disclosure describes systems configured to recognize indicators of a medical condition within a diagnostic image and predict the progression of the medical condition based on the recognized indicators. The systems can include neural networks trained to extract disease features from diagnostic images and neural networks configured to model the progression of such features at future time points selectable by a user. Modeling the progression may involve factoring in various treatment options and patient-specific information. The predicted outcomes can be displayed on a user interface customized to specific representations of the predicted outcomes generated by one or more of the underlying neural networks. Representations of the predicted outcomes include synthesized future images, probabilities of clinical outcomes, and/or descriptors of disease features that may be likely to develop over time.

Abstract: The present disclosure describes an ultrasound imaging system configured to identify a target placement of an ultrasound probe for viewing a lung pleural line. In some examples, the system may include an ultrasound probe configured to receive ultrasound echoes from a subject to image a region of the subject and a data processor in communication with the ultrasound probe. The data processor may be configured to identify one or more candidate pleural lines and one or more A-lines corresponding to the candidate pleural lines, compute an A-line intensity of at least one of the A-lines, and apply the computed A-line intensity to indicate a target placement of the ultrasound probe for imaging the region for pleural line identification. The system may also include a user interface in communication with the data processor. The user interface may be configured to alert the user of the target placement of the ultrasound probe.

Abstract: Systems and methods of determining a second medical professional having experience relevant to a medical procedure being performed by a first medical professional. A method of determining a second medical professional having experience relevant to a medical procedure being performed by a first medical professional comprises: obtaining a first multi-dimensional embedding vector related to the medical procedure being performed by the first medical professional. The method then comprises obtaining a plurality of candidate multi-dimensional embedding vectors related to medical experience of a corresponding plurality of candidate medical professionals, and using a model to determine the second medical professional from the plurality of candidate medical professionals, based on the first multi-dimensional embedding vector and the plurality of candidate multi-dimensional embedding vectors.

Abstract: A guidance system is configured to detect a current pose of an ultrasound transducer and to determine a movement to achieve a desired pose associated with a desired view or imaging plane of a patients anatomy. In one embodiment, the guidance system includes a processor circuit in communication with the ultrasound transducer. The processor circuit is configured to: receive an input associated with a desired pose of the ultrasound transducer; receive ultrasound imaging data representative of a field of view of the ultrasound transducer in a current pose; determine a movement to align the current pose of the ultrasound transducer with the desired pose; and generate a graphical representation of the movement. The graphical representation shows both the current pose of the ultrasound transducer and the desired pose of the ultrasound transducer. The graphical representation is output to a display in communication with the processor circuit.

Abstract: Ultrasound image devices, systems, and methods are provided. An ultrasound imaging system comprising a processor circuit in communication with an ultrasound transducer array, the processor circuit configured to receive, from the ultrasound transducer array, a set of images of a three-dimensional (3D) volume of a patients anatomy including an anatomical feature; obtain first measurement data of the anatomical feature in a first image of the set of images; generate second measurement data for the anatomical feature in one or more images of the set of images by propagating the first measurement data from the first image to the one or more images; and output, to a display in communication with the processor circuit, the second measurement data for the anatomical feature.

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: 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: The invention provides a method for guiding the acquisition of ultrasound data within a 3D field of view. The method begins by obtaining initial 2D B-mode ultrasound data of a cranial region of a subject from a reduced field of view at a first imaging location and determining whether a vessel of interest is located within the 3D field of view based on the initial 2D B-mode ultrasound data. If the vessel of interest is not located within the 3D field of view, a guidance instruction is generated based on the initial 2D B-mode ultrasound data, wherein the guidance instruction is adapted to indicate a second imaging location to obtain further ultrasound data. If the vessel of interest is located within the 3D field of view 3D Doppler ultrasound data is obtained of the cranial region from the 3D field of view.

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: A method (20) and device for deriving an estimate of intracranial blood pressure based on motion data for a wall of an intracranial blood vessel, intracranial blood flow velocity, and a blood pressure signal measured at a location outside the brain. The method is based on identifying (28) a time offset between the two intracranial signals (vessel wall movement and vessel blood flow), and then applying (30) this offset to the blood pressure signal acquired from outside the brain to obtain a fourth signal, indicative of estimated intracranial blood pressure.

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 describes imaging systems configured to identify features within image frames and improve the frames by implementing image quality adjustments. An ultrasound imaging system can include a transducer configured to acquire echo signals responsive to ultrasound pulses transmitted toward a target. The system can also include a user interface configured to display an image and one or more processors configured to identify one or more features within the image. The processors can cause the interface to display elements associated with at least two image quality operations specific to the identified feature. A first image quality operation can include a manual adjustment of a transducer setting, and a second image quality operation can include an automatic adjustment of the identified feature derived from reference frames including the identified feature. The processors can receive a user selection of one or more elements and apply the operations to modify the image.

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 configured to identify and remove image artefacts from ultrasound image frames by applying a neural network to the frames. Systems may include an ultrasound transducer configured to acquire echo signals responsive to ultrasound pulses transmitted toward a target region. One or more processors communicatively coupled with the ultrasound transducer may be configured to generate an image frame from the ultrasound echoes and apply a neural network to the image frame. The neural network determines whether an artefact is present in the image frame, and identifies the type of artefact detected. The processors can also generate an indicator conveying the presence of the artefact, which can be displayed on a user interface. The processors can further generate an instruction for adjusting the ultrasound transducer based on the presence and type of artefact present within the image frame.

Abstract: The present disclosure describes an ultrasound imaging system configured to identify a target placement of an ultrasound probe for viewing a lung pleural line. In some examples, the system may include an ultrasound probe configured to receive ultrasound echoes from a subject to image a region of the subject and a data processor in communication with the ultrasound probe. The data processor may be configured to identify one or more candidate pleural lines and one or more A-lines corresponding to the candidate pleural lines, compute an A-line intensity of at least one of the A-lines, and apply the computed A-line intensity to indicate a target placement of the ultrasound probe for imaging the region for pleural line identification. The system may also include a user interface in communication with the data processor. The user interface may be configured to alert the user of the target placement of the ultrasound probe.