A method of determining a position of a target using a metric comprises receiving a plurality of ultrasound signals representative of ultrasound energy received from the target and, for each of a plurality of different foci, adjusting the ultrasound signals in dependence on the focus and determining a value of the metric using the adjusted ultrasound signals. The method further comprises using the determined values for the metric for the different foci to determine a position of the target.

Disclosed are devices, systems, and methods for multi-modal, wearable sensors, including an electrochemical-ultrasonic transducer-based sensor, that can simultaneously detect and monitor one or more bio-analyte markers and one or more physiological markers. In some aspects, a wearable, acoustic-electrochemical sensor device includes a flexible substrate, one or more electrochemical sensors disposed on the flexible substrate, a physiological sensor comprising an array of acoustic transducers disposed on the flexible substrate, wherein the sensor device is operable to simultaneously detect and monitor one or more analyte markers and physiological markers including hemodynamic parameters.

An ultrasound training model which exhibits optically clear soft tissue-mimicking materials that are simultaneously acoustically scattering and self-healing to needle punctures. An exemplary embodiment is disclosed that comprises an embedded bone-mimicking spine model and a dual-purpose lid that may be used as a friction surface mat. Various embodiments of the training model materials are disclosed.

A medical probe includes a shaft and a distal-end assembly. The shaft is configured for insertion into an organ of a body. The distal-end assembly is fitted at a distal end of the shaft. The distal-end assembly includes (a) a substrate, (b) a two-dimensional (2D) ultrasound transducer array located on the substrate, and (c) a sensor, which is also located on the substrate, the sensor configured to output signals indicative of a position and an orientation of the 2D ultrasound transducer array inside the organ.

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.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.

A method for estimating an ultrasonic attenuation parameter of a region in a medium includes a transmission step in which at least one pulse is transmitted in the medium by a transducer, and a reception step in which data is acquired by a transducer in response to the pulse. The method includes a processing step in which the data is processed by the processing unit for providing backscattered acquisition data of the region, and a function determination step in which an auto-correlation function of the backscattered acquisition data is determined which is a function of depth in the spatio-temporal domain, the autocorrelation function being determined at a lag of zero. The method includes an attenuation estimation step in which an ultrasonic attenuation parameter is estimated based on said auto-correlation function. The method is implemented by a processing unit associated to at least one ultrasound transducer.

An ultrasound test phantom includes a block of human-tissue-mimicking material having a top surface and spaced-apart groups of targets embedded therein. Each group is located at a unique depth region within the block as measured from the top surface. The targets in each group include a first linear target spaced-apart from a second linear target. For each group, the first linear target extends in a first direction at a first depth of the depth region associated therewith, and the second linear target extends in a second direction at a second depth of the depth region associated therewith. When viewed from the block's top surface, a crossing point is defined where the first direction and second direction cross at an angle between 10° and 170°. For each group, the crossing point is located along a line perpendicular to the block's top surface.