A method for testing the accuracy and the performance of an ultrasound transducer able to generate cavitation bubbles by placing in a volume of an ultrasound-transmitting medium an elastomer block at an expected cavitation region of the ultrasound transducer, activating the ultrasound transducer so as to generate cavitation bubbles in the elastomer block, detecting in the elastomer marks corresponding to the generated cavitation bubbles and deducing from the marks the three-dimensional characteristics of a real cavitation region of the ultrasound transducer. A system comprising volume of an ultrasound-transmitting medium, an elastomer block disposed in the volume of an ultrasound-transmitting medium, and an ultrasound transducer is also provided.

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.

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 present invention relates to an ultrasound-based system for localizing a medical device within the field of view of an ultrasound imaging probe. A localization system is provided that includes at least three ultrasound emitters that are arranged on a frame; and a position triangulation unit. The frame is adapted for attachment to an ultrasound imaging probe. The position triangulation unit determines a spatial position of the ultrasound detector relative to the at least three ultrasound emitters based on signals received from an ultrasound detector that is attached to the medical device. The frame includes a detachable reference volume comprising a background volume and an inclusion or void. When the detachable reference volume is attached to the frame and the frame is attached to the ultrasound imaging probe the inclusion or void provides a corresponding image feature within the field of view of the ultrasound imaging probe for use in calibrating the field of view of the ultrasound imaging probe with the coordinate system of the localization system.

A system and method for quantitative validation of the quality of image-guided interventional procedures combine image data on the procedure with position tracking of the interventional instrument and calculate quantitative measures such as excursions of the interventional instrument from a desired position or track, procedure times and measurements of distances advanced and withdrawn. The quantitative metrics may be graphically displayed and stored. Metrics obtained for multiple users may be stored and statistically processed to derive measures of performance applicable to individuals or groups of clinicians.

An anatomical model includes a first container, a second container disposed within the first container, and a third container disposed within the second container. The second container and the third container simulate components of kidney anatomy, and the model includes ballistics gel to improve echogenicity during an ultrasound-guided training procedure using the model.

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.