An imaging steering apparatus includes sensors and an imaging processor configured for: acquiring, via multiple ones of the sensors and from a current position (322), and current orientation (324), an image of an object of interest; based on a model, segmenting the acquired image; and determining, based on a result of the segmenting, a target position (318), and target orientation (320), with the target position and/or target orientation differing correspondingly from the current position and/or current orientation. An electronic steering parameter effective toward improving the current field of view may be computed, and a user may be provided instructional feedback (144) in navigating an imaging probe toward the improving. A robot can be configured for, automatically and without need for user intervention, imparting force (142) to the probe to move it responsive to the determination.

The present disclosure describes ultrasound systems and methods configured to determine the elasticity of a target tissue. Systems can include an ultrasound transducer configured to acquire echoes responsive to ultrasound pulses transmitted toward the tissue, which may include a region of increased stiffness. Systems can also include a beamformer configured to control the transducer to transmit a push pulse into the tissue, thereby generating a shear wave in the region of increased stiffness. The beamformer can be configured to control the transducer to emit tracking pulses adjacent to the push pulse. Systems can further include a processor configured to determine a displacement amplitude of the shear wave and based on the amplitude, generate a qualitative tissue elasticity map of the tissue. The processor can combine the qualitative map with a quantitative map of the same tissue, and based on the combination, determine a boundary of the region of increased stiffness.

For three-dimensional segmentation from two-dimensional intracardiac echocardiography imaging, the three-dimension segmentation is output by a machine-learnt multi-task generator. The machine-learnt multi-task generator is trained from 3D information, such as a sparse ICE volume assembled from the 2D ICE images. The machine-learnt multi-task generator is trained to output both the 3D segmentation and a complete volume. The 3D segmentation may be used to project to 2D as an input with an ICE image to another network trained to output a 2D segmentation for the ICE image. Display of the 3D segmentation and/or 2D segmentation may guide ablation of tissue in the patient.

A facility for processing a medical imaging image is described. The facility applies to the image a first machine learning model trained to recognize a view to which an image corresponds, and a second machine learning model trained to identify any of a set of anatomical features visualized in an image. The facility accesses a list of permitted anatomical features for images corresponding to the recognized view, and filters the identified anatomical features to exclude any not on the accessed list. The facility causes the accessed image to be displayed, overlaid with a visual indication of each of the filtered identified anatomical features.

The medical imaging apparatus of the present invention includes a bath tank for accommodating a test subject, the test subject being at least a portion of a body of a human subject; a measurement device movable in a predetermined direction, the measurement device including ab group of elements to emit a radiation wave into the bath tank and receive a scattered radiation wave; and a control unit for measuring by the measurement device when at least one of a location of the test subject within a plane orthogonal to the predetermined direction, a location of the test subject in the predetermined direction, and continuity of data measured by the measurement device satisfies a predetermined condition. Because of this configuration, the medical imaging apparatus is able to acquire data covering the entire measurement target site.

Aspects of the technology described herein relate to techniques for calculating, during imaging, a quality of a sequence of images collected during the imaging. Calculating the quality of the sequence of images may include calculating a probability that a medical professional would use a given image for clinical evaluation and a confidence that an automated analysis segmentation performed on the given image is correct. Techniques described herein also include receiving a trigger to perform an automatic measurement on a sequence of images, calculating a quality of the sequence of images, determining whether the quality of the sequence of images exceeds a threshold quality, and performing the automatic measurement on the sequence of images based on determining that the quality of the sequence of images exceeds the threshold quality.

An interpolation unit generates a vector array representing a movement destination of a representative point array. A smoothing unit smoothes a tangential component and a normal component of each vector of the vector array, to generate a smoothed vector array. An aligning unit generates a new representative point array based on the smoothed vector array. In this process, alignment is performed for each representative point sequence. A tracking image is created based on the new representative point array.

A combined ultrasonic and endoscopy system includes a cannula with a distal tip configured for insertion into an internal organ or other internal body structure. The distal tip is configured to include both an ultrasound probe head and a camera module. The ultrasonic and direct vision endoscopy images can be simultaneously displayed to a user on a display monitor. The ultrasound probe head can be rotated and steered to scan any location in the human organ cavity. The ultrasound probe can be re-usable or single use. The endoscopy system can be configured with a handheld portion that includes a re-usable handle portion and a single use portion that is configured to be disposed of following a single use. The system can also be configured using a conventional re-usable endoscope with working channels and an endoscopy processing tower system.

Systems and methods are provided for motion corrected wide-band pulse inversion ultrasonic imaging. A first pulse is transmitted, a second pulse is then transmitted after a delay, with the second pulse having different polarity. Echoes of the first pulse and the second pulse are received, using a reception bandwidth that enables capturing at least a portion of a fundamental portion of each pulse. The echoes are processed, and corresponding ultrasound images are generated based on processing. The processing includes determining displacement data between the first pulse echo and the echo of the second pulse for at least one structure in an imaged area; determining one or more displacement corrections based on the displacement data; applying at least one displacement correction to at least one of the first pulse echo and the echo of the second pulse; and combining the first pulse echo and the echo of the second pulse.

Ultrasound image devices, systems, and methods are provided. An ultrasound imaging system comprising a processor circuit configured to receive, from an ultrasound transducer array, a plurality of images of a patient body at different imaging planes; determine a first imaging plane based on a first image of the plurality of images by applying a first predictive network, wherein a second image of the plurality of images is based on the first imaging plane, and wherein the second image includes an imaging view of at least one of an anatomy of the patient body or a medical device within the anatomy; apply a second predictive network to the second image to generate segmentation data; and output, to a display, a displayed image including an indication of a first portion of the at least one of the anatomy or the medical device based on the segmentation data.