Embodiments described herein include apparatus that includes an electrical interface and a processor. The processor is configured to receive, via the electrical interface, a first signal that indicates a time-varying force that was applied to a portion of tissue, and one or more second signals that are derived from ultrasound reflections received from the portion of tissue. The processor is further configured to learn, from the first signal and the second signals, a dependency of a thickness of the portion of tissue on the force applied to the portion of tissue. Other embodiments are also described.

An automated measurement device and method for coronary artery disease scoring is disclosed. An example device includes a processor configured to obtain a computerized model of a plurality of vascular segments of a patient and create an unstenosed computerized model from the computerized model by virtually enlarging at least some locations of the vascular segments of the computerized model. The processor also determines vascular state scoring tool (“VSST”) scores based on characteristics of vascular locations along the vascular segments. The processor further determines a severity of stenosis for the vascular locations based on comparisons of first blood flow parameter values at the vascular locations in the computerized model to corresponding second blood flow parameter values at the same vascular locations in the unstenosed computerized model. A user interface of the device displays the severity of stenosis in conjunction with the VSST scores for the vascular locations.

An ultrasound imaging system includes a processor programmed to generate an anatomy image and a number of needle frames at different transmit beam angles. The system analyzes the data in the needle frames and selects segments therein that are identified as likely representing an interventional instrument. Data from one or more needle frames are blended with the data for the anatomy image of the tissue to create a composite image of the tissue and the interventional instrument.

An ultrasound imaging system includes a processor programmed to generate an anatomy image and a number of needle frames at different transmit beam angles. The system analyzes the data in the needle frames and selects segments therein that are identified as likely representing an interventional instrument. Data from one or more needle frames are blended with the data for the anatomy image of the tissue to create a composite image of the tissue and the interventional instrument.

Disclosed are systems and methods using ultrasound to predict if a patient's cardiac stroke volume will increase with a fluid bolus. Ultrasound measures are taken before administering a fluid bolus, including measurement of the left ventricular outflow tract velocity time integral (LVOT VTI), and venous measurements of the internal jugular vein. Data collected from such ultrasound scan is then used to predict the patient's cardiac volume response in the event that a fluid bolus is administered to that patient.

For parametric ultrasound imaging with an ultrasound scanner, the values for multiple parameters are determined for tissue of a patient using ultrasound. The determination may be in response to a single activation, avoiding the user having to reconfigure and activate separately for each parameter. To assist in diagnosis, one or more indicators of quality of the measurements of the parameters are calculated and displayed to the user. To further assist in diagnosis, the measurement values for the patient are displayed relative to published or population values.

Image analysis method for quantitative assessment of a degree of coordination of uterine motion, comprising: obtaining a recording of a uterus; tracking, in the recording, at least two propagation waves of uterine motion in at least two different regions of the uterus and/or on at least two different times; and determining the degree of coordination of the uterine motion based on a similarity measure of at least two characteristics of the at least two propagation waves.

Various approaches for computationally characterizing tissue in an anatomic target region include generating multiple sonications to transient acoustic reflectors at or proximate to the target region; measuring reflection signals of the sonications off the transient acoustic reflectors; based on the measurements, identifying the reflection signals originating from single transient acoustic reflectors; and based at least in part on the identified reflection signals, generating a digital map including a tissue characteristic.

An ultrasound diagnostic apparatus (1) includes an ultrasound probe (2) and a diagnostic apparatus body (3) connected to the ultrasound probe (2), in which the diagnostic apparatus body (3) includes a monitor (15) on which an ultrasound diagnosis image and a plurality of diagnostic finding icons corresponding to a plurality of determined diagnostic findings are displayed, a similarity determination unit (18) that determines similarity between the ultrasound diagnosis image and each of a plurality of image patterns corresponding to the plurality of diagnostic findings, and a display position changing unit (19) that changes display positions of the plurality of diagnostic finding icons on the monitor in accordance with the similarity determined by the similarity determination unit (18).

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, perform computational fluid dynamics analysis, facilitate assessment of risk of heart disease and coronary artery disease, enhance drug development, determine a CAD risk factor goal, provide atherosclerosis and vascular morphology characterization, and determine indication of myocardial risk, 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.