An apparatus includes an imaging probe and is configured for dynamically arranging presentation of visual feedback (144) for guiding manual adjustment, via the probe, of a location, and orientation, associated with the probe. The arranging is selectively based on comparisons (321) between fields of view of the probe and respective results of segmenting image data acquired via the probe. In an embodiment, the feedback does not include (175) a grayscale depiction of the image data. Coordinate system trans formations corresponding to respective comparisons may be computed. The selecting may be based upon and dynamically responsive to content of imaging being dynamically acquired via the probe.

A data processing method for calculating the contact position of a medical ultrasound transceiver on the head of a patient, comprising the steps of: a) acquiring ROI data which represent a region of interest (ROI) corresponding to at least a part of a vessel in a vascular structure; b) acquiring contact region data which represent a contact region for the ultrasound transceiver on the head, wherein the contact region corresponds to one or more acoustic windows; c) determining at least one target point in the region of interest; d) determining at least two entry points on the contact region; e) calculating a set of lines which comprises the lines between the two points of each respective possible pair consisting of one entry point and one target point; f) eliminating lines which pass through a bony structure other than the bone immediately beneath the contact region; g) calculating a score for each of the remaining lines; and h) selecting the entry point of the line with the highest score as the contact position of the ultrasound transceiver.

In some examples, a CMUT array may include a plurality of elements, and each element may include a plurality of sub-elements. For instance, a first sub-element and a second sub-element may be disposed on opposite sides of a third sub-element. In some cases, the third sub-element may be configured to transmit ultrasonic energy at a higher center frequency than at least one of the first sub-element or the second sub-element. Further, in some instances, the sub-elements may have a plurality of regions in which different regions are configured to transmit ultrasonic energy at different resonant frequencies. For instance, the resonant frequencies of a plurality of CMUT cells in each sub-element may decrease in an elevation direction from a center of each element toward the edges of the CMUT array.

An ultrasound diagnosis apparatus includes: a controller configured to recognize an object included in an ultrasound image and search at least one piece of texture information corresponding to the recognized object; a display configured to display the ultrasound image and the searched at least one piece of texture information; a user interface configured to receive an input for selecting one piece of texture information from among the searched at least one piece of texture information; and an image processor configured to perform texture mapping of the selected piece of texture information onto at least one region in the ultrasound image of the object.

An intravascular ultrasound probe is disclosed, incorporating features for utilizing an advanced transducer technology on a rotating transducer shaft. In particular, the probe accommodates the transmission of the multitude of signals across the boundary between the rotary and stationary components of the probe required to support an advanced transducer technology. These advanced transducer technologies offer the potential for increased bandwidth, improved beam profiles, better signal to noise ratio, reduced manufacturing costs, advanced tissue characterization algorithms, and other desirable features. Furthermore, the inclusion of electronic components on the spinning side of the probe can be highly advantageous in terms of preserving maximum signal to noise ratio and signal fidelity, along with other performance benefits.

A system is provided for determining the movement of an interventional device inside a lumen. The interventional device comprises a distal portion inside the lumen and a proximal portion outside the lumen and the system comprises a processor. The processor is configured to receive one or more images from an ultrasound transducer, wherein the images are representative of the lumen with the interventional device inside and input the one or more images into a machine learning algorithm. The machine learning algorithm is trained to learn the relationship between the content of one or more images of an interventional device inside a lumen and the displacement of the distal portion of the interventional device in an elevational direction parallel, or almost parallel, to the direction of movement of the distal portion of the interventional device and output a one dimensional estimated displacement corresponding to the movement of the distal portion of the interventional device in the elevational direction for one or more of the images.

User feedback on acquisition of ultrasound data may be provided to a user. The feedback may indicate a quality of the acquisition and/or the reliability of the measurements calculated from the ultrasound data, for example, the volume flow measurements calculated from Doppler data. Various quality factors such as a signal-to-noise ratio (SNR), motion, Doppler angle, vessel size, vessel depth, and/or variance in velocity values may be determined to provide an indication of quality of the acquisition. The quality factors may be provided individually or in combination. In some examples, one or more quantitative values of the quality factors may be provided. In some examples, one or more qualitative indications of the quality of the acquisition may be provided.

A method includes: obtaining an ultrasound image of an anatomical area from an ultrasound imaging device; inputting the ultrasound image into a first stage of a convolutional neural network, the first stage configured to determine key-point locations of the anatomical area; generating, for each of the key-point locations, a cropped region of the ultrasound image; inputting each of the cropped regions into a second stage of the convolutional neural network, the second stage configured to locate an anatomical landmark of the anatomical area; and outputting a location of the anatomical landmark.

Provided are systems for analyzing cellular distribution in an engineered tissue sample, which optionally is a bioprinted organ or tissue sample. In some embodiments, the systems include an ultrasound imaging system and a processing unit configured with software that permits analysis of images acquired from the engineered tissue sample in order to output desired characteristics thereof. In some embodiments, the systems also include a bioreactor for engineering a tissue sample and a pump configured to regulate flow of fluids and reagents into and out of the bioreactor, wherein at least one surface of the bioreactor includes a window that is acoustically transparent to ultrasound waves. Also provided are systems for analyzing cell distribution in an engineered tissue sample and methods for analyzing distribution of cells in an engineered tissue sample present within a bioreactor.

An image processing method and an image processing apparatus are provided. The image processing method measures a myocardial performance index (MPI), the image processing method including: obtaining a region of interest (ROI) for measuring the MPI, based on signal levels of an input signal and an output signal of a heart spectrum image; obtaining a plurality of marker areas, within the obtained ROI, wherein at least one marker for measuring the MPI is located in each of the plurality of marker areas, based on at least one from among a feature value of the input signal and a feature value of the output signal; and obtaining the at least one marker for each of the plurality of marker areas.