Siemens Medical Solutions Usa, Inc.

Smart Home Sentry, Inc.

Agility4life

Seshadri Srinivasan

Ismayil M Guracar

Kutay F Ustuner

Uday Kiran Chaka

Patrick J Phillips

Saurabh Datta

Ramesh Sastry

Ritesh Agarwal

James Garner

Bhaskar S Ramamurthy

Albert Gee

Gregory L Holley

Paul Donald Freiburger

Charles E Bradley

Helene C Houle

Bimba Rao

Rickard C Loftman

Bruce A McDermott

Xiaohui Hao

Patric Ljung

Christophe Duong

Merv Mencias Smith-Casem

Steve Henderson

A method and a target monitoring system (TMS) to generate descriptive parameters of one or more target objects in a region are provided. The TMS dynamically receives first data comprising image data and/or audio data of the target objects in the region and second data comprising circumstantial information related to the first data from one or more sensors positioned in one or more spatial directions in the region over a network. The TMS filters the dynamically received first data and identifies the target objects in the region using image data extracted from the filtered data. The TMS generates descriptive parameters associated with each identified target object in the region using the filtered first data and the circumstantial information.

Methods and system for monitoring an environment

A method and a biomechanical parameter determination system (BPDS) for determining biomechanical parameters of one or more target objects in a region are provided. The BPDS dynamically receives first data including temperature data and/or motion data of target objects in the region from first sensors and dynamically receives second data including optical images, sound data, amplitude, and/or distance of the target objects in the region from second sensors, over a network. The BPDS filters the dynamically received first data and second data of the target objects and identifies one or more target objects using the temperature data and/or motion data of the target objects. The BPDS determines one or more biomechanical parameters including, for example, positions, acceleration, walking speed, fall, posture, etc., associated with each of the identified target objects in the region using the filtered first data and/or the filtered second data for emergency alerting and/or health assessment.

Biomechanical parameter determination for emergency alerting and health assessment

Spatially distinct Spectral Doppler information is displayed. A spectrum is determined for each of a plurality of spatial locations, such as associated with different receive beams. Given a plurality of spectra at different spatial locations, an image may be generated as a function of the spectra. For example, a two-dimensional image has display values for different pixels or locations derived from one or more characteristics of the spectra, such as the maximum velocity with energy above a threshold for each location. As another example, the spectrum from the set of spectra with the highest maximum velocity is selected for generating a spectral strip display.

Spectral Doppler with multiple spatially distinct gates

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

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

Adaptive volume rendering is provided for medical diagnostic ultrasound. The opacity of B-mode data is set relative to the opacity of Doppler data. The opacities for the different types of data are set as a function of ray depth. For example, the opacity of B-mode data near a tissue border is set to be more opaque than for tissue away from the border, and the opacity for flow data near a flow border is set to be less opaque than for flow away from the border. An image is rendered using a rendering parameter set as a function of ray depth, B-mode information and Doppler information. Other processes for enhancing and/or using rendering may be used.

Adaptive volume rendering for ultrasound color flow diagnostic imaging

A volumetric method for 2-D flow imaging is provided in medical diagnostic ultrasound. Flow data for a volume is acquired. For more rapid acquisition, broad beam transmission and reception along many scan lines distributed in the volume is used. The volumetric flow data is filtered, such as by calculating statistical information, to generate a planar/2-D flow image. The statistical information from the three-dimensional flow data is used to determine the display values for the flow imaging.

Multi-plane/multi-slice processing for 2-D flow imaging in medical diagnostic ultrasound

Motion imaging in medical diagnostic ultrasound is adaptive. Clutter or threshold processing adapts as a function of data for different locations or for different times. Spatial filtering adapts as a function of data at different spatial locations at a same time. The steering angle may be set as a function of region based on the vessel orientation and maximum velocity. The region of interest for motion imaging may be expanded to counteract, at least in part, a shift due to the steering angle.

Adaptive motion imaging in medical diagnostic ultrasound imaging

Orientation is automatically identified in medical diagnostic ultrasound image. An area or volume based process, such as region shrinking or using locations associated with flow or tissue structure, determines a direction or orientation. Real-time imaging in B-mode and/or flow mode may be enhanced based on the direction or orientation.

Automatic identification of orientation in medical diagnostic ultrasound

A method is provided to improve the frame-rate in color-flow ultrasound imaging using simultaneous spatially-distinct transmit beams with one or more frequency bands per transmit beam. Pulses of different center frequencies are used simultaneously in different (lateral and/or elevational) directions, thereby reducing the scanning time and improving the frame-rates. Optionally, a multi-modal pulse is used, and flow is estimated separately for the different frequencies. The flow estimates for these pulses are appropriately combined to improve low-velocity sensitivity and to reduce aliasing. A flow sample count with two or more different pulse repetition intervals can be used to further improve low-flow sensitivity and minimize aliasing.

Ultrasound color flow imaging at high frame rates

Velocity scale is automatically determined for ultrasound imaging. Receive signals corresponding to different pulse repetition intervals are used to estimate velocities. The different velocities are used to determine the velocity scale. For example, the different pulse repetition intervals are obtained by selecting different sets of signals from a group of signals for a scan line. As another example, the different pulse repetition intervals are obtained by transmitting for a scan line with changing, such as increasing, pulse repetition intervals. The lower pulse repetition interval of a pair of intervals associated with a sign change in the phase is used for the velocity scale. In yet another example, at least two demodulation frequencies are used for a broadband transmit in at least two sets, each set with a different pulse repetition interval. Velocities are estimated for a greater number of pulse repetition intervals than transmitted using the broadband pulses.

Automatic velocity scale identification for medical diagnostic ultrasound

Diagnostic ultrasound flow imaging is performed with coded excitation pulses. Due to the use of frequency coded excitation pulses, flow information may suffer from spatial misregistration and estimate errors. Spatial position shift in flow data is offset for alignment with B-mode or other imaging. The flow estimates are compensated for the imaging center frequency variation with depth. The wide bandwidth information available due to coded excitation may allow anti-aliasing by estimating velocities from two frequency bands.

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Mountain View, CA, United States of America

Seshadri Srinivasan