Siemens Medical Solutions Usa, Inc.

Siemens Corporation

Siemens Aktiengesellschaft

University of Cincinnati

Saurabh Datta

Bogdan Georgescu

Yang Wang

Dorin Comaniciu

Seshadri Srinivasan

Paul Donald Freiburger

Ingmar Voigt

Razvan Ioan Ionasec

Helene C Houle

T Douglas Mast

Christy Holland

Wilko Gerwin Wilkening

Dime Vitanovski

Nikolas Ivancevich

Chi-Yin Lee

Kevin J Haworth

Kathryn Elizabeth Hitchcock

Christophe Duong

Christy K Holland

Methods and systems for passively detecting stable cavitation and enhancing stable cavitation during sonothrombolysis are provided. The method of passively detecting stable cavitation includes providing a determined level of ultrasonic energy and detecting a scattered level of ultrasonic energy. The system for inducing and passively detecting stable cavitation includes a dual-element annular transducer array configured to provide a fundamental ultrasonic frequency and to detect an ultrasonic frequency that is a derivative of the fundamental frequency. The method of enhancing stable cavitation includes administering a nucleating agent and a thrombolytic agent to a treatment zone, providing a determined level of ultrasonic energy, and detecting a scattered level of ultrasonic energy.

Ultrasound-mediated inducement, detection, and enhancement of stable cavitation

Spatially distinct Spectral Doppler information is acquired. Spatially distinct transmit beams are formed at a same time or in parallel. One or more receive beams are formed in response to each transmit beam, providing samples for a plurality of laterally spaced locations. A spectrum is determined for each of a plurality of spatial locations. In another approach, samples are acquired for different regions at different times. The scanning for each region is interleaved based on the anatomic operation. Since spectral estimation relies on a time-continuous series of transmission and reception, the scanning for a region occurs over a sufficient period for spectral estimation before the scanning for a different region occurs. By using anatomic operation, sufficient time is provided for spectral estimation. Due to anatomic operation, different regions are associated with flow at different times.

Multiple beam spectral doppler in medical diagnostic ultrasound imaging

Volumetric quantification is provided in medical diagnostic ultrasound. By acquiring both B-mode and color flow data without stitching or acquiring in real-time at tens of volumes a second, more reliable quantification may be provided. Using multiple regions of interest in a volume may allow for more accurate and/or complete flow information, such as averaging flow from different locations in the same structure (e.g., use preservation of mass to acquire multiple measures of the same flow).

Volumetric is quantification for ultrasound diagnostic imaging

Velocities are unaliased using conditional random fields. To constrain the energy minimization function, a global term includes a measure of a level of aliasing. In one example, the measure of the level of aliasing is based on a change in volume, such as the volume of the left ventricle. The unaliasing is performed along one or more surfaces, such as surfaces intersecting the mitral annulus and the left ventricle outflow tract. The anatomy used is identified and/or tracked using one or more machine-learnt detectors. Both B-mode and velocity information may be used for detecting the anatomy.

Flow quantification in ultrasound using conditional random fields with global consistency

A mitral valve is detected in transthoracic echocardiography. The ultrasound transducer is positioned against the chest of the patient rather than being inserted within the patient. While data acquired from such scanning may be noisier or have less resolution, the mitral valve may still be automatically detected. Using both B-mode data representing tissue as well as flow data representing the regurgitant jet, the mitral valve may be detected automatically with a machine-learnt classifier. A series of classifiers may be used, such as determining a position and orientation of a valve region with one classifier, determining a regurgitant orifice with another classifier, and locating mitral valve anatomy with a third classifier. One or more features for some of the classifiers may be calculated based on the orientation of the valve region.

Mitral valve detection for transthoracic echocardiography

Flow estimation is provided. The flow is predicted. A mathematical, logic, machine learning or other model is used to predict flow. For example, the boundary conditions associated with a previous flow, the previous flow, and current boundary conditions are used to predict the current flow. The current flow is corrected using the predicted flow. Velocities may be unaliased based on the predicted flow. The predicted flow may replace the current flow. Prediction may additionally or alternatively be used in determination of lateral or elevational flow.

Prediction-based flow estimation for ultrasound diagnostic imaging

A method quantifies cardiac volume flow for an imaging sequence. The method includes receiving data representing three-dimensions and color Doppler flow data over a plurality of frames, constructing a ventricular model based on the data representing three-dimensions for the plurality of frames, the ventricular model including a sampling plane configured to measure the cardiac volume flow, computing volume flow samples based on the sampling plane and the color Doppler flow data, and correcting the volume flow samples for aliasing based on volumetric change in the ventricular model between successive frames of the plurality of frames.

Cardiac flow quantification with volumetric imaging data

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Cupertino, CA, United States of America

Saurabh Datta