Exo Imaging, Inc.

A method includes generating a series of two-dimensional (2D) ultrasound images of a tissue volume associated with a plurality of positions, respectively, along a scanning direction of the tissue volume. The method includes, for a pair of consecutive 2D ultrasound images, using a deep neural network to classify a difference between the pair of consecutive 2D ultrasound images of the series of 2D ultrasound images. The deep neural network is trained to classify differences between images based on one of a plurality of predefined classes. The method includes modifying a number of 2D ultrasound images in the series of 2D ultrasound images based on the classification of the difference between the pair of consecutive 2D ultrasound images of the series of 2D ultrasound images, thereby producing a modified series of 2D ultrasound images. The method includes performing a measurement of tissue using the modified series of 2D ultrasound images.

A method for cross-referencing of 2D ultrasound scans of a tissue volume comprises generating first and second 2D representations of a target anatomy, respectively, from first and second series of 2D ultrasound images of the tissue volume generated. The method includes generating first and second simulated 2D representations of the target anatomy, respectively, from the second and first series of 2D ultrasound images. The 2D and simulated 2D representations are processed to at least substantially match pixel positions in the 2D and simulated 2D representations associated with the target anatomy. The method includes determining first and second correspondence transformation matrices from the processed 2D and simulated 2D representations, and using the first and second correspondence transformation matrices to determine, for a location in the second series of 2D ultrasound images associated with the target anatomy, a corresponding location in the first series of 2D ultrasound images.

A final ultrasound image can be formed by receiving an actual ultrasound image from an ultrasound imaging device, the actual ultrasound image being formed using parameters, creating a synthetic ultrasound image using a simulation model based on the parameters; and forming the final ultrasound image based on the synthetic ultrasound image and the actual ultrasound image.

Disclosed herein are portable ultrasound imaging systems for thumb-dominant operations comprising: a portable ultrasound probe, wherein the portable ultrasound probe is configured to be operable using a first hand of the user; a mobile device comprising a mobile application installed thereon, the mobile application comprising a user interface, the mobile application configured to be operable using a second hand of the user and; and direct electronic communication between the portable ultrasound probe and the mobile device, the direct electronic communication configured to allow a user to control an operation of the portable ultrasound probe for imaging via user interaction with the user interface

A method includes obtaining a first frame of a probed region acquired using a first probe device at a first time and processing, using computer vision or machine learning, the first frame to obtain a first set of attributes of the first frame. Processing the first frame includes identifying one or more features from the first frame and mapping the one or more features onto a template that includes one or more markers that identify locations of one or more anatomical regions of interest that include a first anatomical region of interest in which a respective attribute is located. The method includes, in accordance with a determination that the first frame contains the respective attribute present in a second frame acquired at different time: displaying, on a user interface, information related to differences in the respective attribute based on the first frame and the second frame.

A portable ultrasound imaging system for thumb-dominant operations can use a portable ultrasound probe that is configured to be operable using a first hand of the user. The system can use a mobile device that has a mobile application installed thereon. The mobile application can have a user interface and be configured to be operable using a second hand of the user. The system can permit direct electronic communication between the portable ultrasound probe and the mobile device and allow a user to control an operation of the portable ultrasound probe for imaging via user interaction with the user interface.

A method includes generating a series of two-dimensional (2D) ultrasound images of the tissue volume associated with a plurality of positions, respectively. The method includes, for a respective pair of consecutive 2D ultrasound images of the series of 2D ultrasound images: generating a difference image from the respective pair of consecutive 2D ultrasound images; estimating, using a deep neural network, an estimated distance between the positions associated with the respective pair of consecutive 2D ultrasound images based on a classification of the difference image, wherein the deep neural network is trained to classify the difference image based on one of a plurality of predefined classes. The method includes rendering the 3D ultrasound image of the tissue volume based on the series of 2D ultrasound images and the estimated distance.

An ultrasound imaging system is controlled by pose and/or gesture detection. The system may detect poses or gestures performed by a user, e.g., using a machine learning (ML) model that may include a neural network, and implement a command associated with the detected pose or gesture to control the ultrasound imaging system.

Described are ultrasound transducer modules and handheld ultrasound imagers including thermal and acoustic management features to produce high quality ultrasound images in a portable, handheld form factor.

A method includes obtaining a first frame of a probed region acquired using a first probe device at a first time, and a first set of control parameters used to acquire the first frame. Processing the first frame includes segmenting features from the first frame after first probe-specific effects are reduced in the first frame based on the first set of control parameters. The method includes, when the first frame contains a respective attribute present in a second frame acquired at a time earlier than the first time using a second set of control parameters, displaying information related to differences in the respective attribute in the first frame and the second frame. The second frame is obtained by processing the one or more features segmented from the second frame after one or more second probe-specific effects are reduced in the second frame.