Yang Wang, Age 46New Brunswick, NJ

2007016, Jul 12, 2007

Jan 9, 2006

11/306733

Frederick Mo - Short Hills NJ, US
Yang Wang - Princeton NJ, US

UTSTARCOM, INC. - Alameda CA

G06F 15/173

709224000

A network element () uses a non-volatile memory () to store indicia of reboots. The indicia of reboots are communicated over a network to a network manager (). The network manager () compares the indicia of reboots and a prior indicia of reboots to assess whether a network element () has rebooted. The network elements preferably store a count of reboots as the indicia.

2010024, Sep 23, 2010

Feb 8, 2010

12/702036

Ingmar Voigt - Erlangen, DE
Yang Wang - Plainsboro NJ, US
Bogdan Georgescu - Plainsboro NJ, US
Helene C. Houle - San Jose CA, US
Dorin Comaniciu - Princeton Junction NJ, US
Fernando Vega-Higuera - Erlangen, DE

A61B 8/14
G06K 9/62

600443, 382128

Heart valve operation is assessed with patient-specific medical diagnostic imaging data. To deal with the complex motion of the passive valve tissue, a hierarchal model is used. Rigid global motion of the overall valve, non-rigid local motion of landmarks of the valve, and surface motion of the valve are modeled sequentially. For the non-rigid local motion, a spectral trajectory approach is used in the model to determine location and motion of the landmarks more efficiently than detection and tracking. Given efficiencies in processing, more than one valve may be modeled at a same time. A graphic overlay representing the valve in four dimensions and/or quantities may be provided during an imaging session. One or more of these features may be used in combination or independently.

2011016, Jun 30, 2011

Dec 30, 2009

12/650528

Yang Wang - Monmouth Junction NJ, US
Wenyu Wang - Monmouth Junction NJ, US

A61B 5/0402

600509

A device, method and system are provided to continuously store and analyze single or multi-lead ambulatory ECG signals. Each ECG device is self-contained, wire-free, reusable and capable of non-invasive continuous recording, analyzing and indexing of ECG data independently. A pair of snap connectors on the device body attaches the device to standard ECG electrodes to minimize noise caused by the wires. Each device contains a real-time clock to link the ECG data and a detected cardiac event with actual date and time. The device contains a USB port to upload the data to a host system. Multiple such devices are wearable by a patient to form a multi-lead ECG system. There is provided a method to continuously record and analyze ECG data using a self-contained, wire-free ECG device or multiple such devices; and further a method to synchronize the ECG data in multiple devices to realize multi-lead ECG recording.

2011030, Dec 8, 2011

Jun 2, 2011

13/151803

Yang Wang - Plainsboro NJ, US
Bogdan Georgescu - Plainsboro NJ, US
Saurabh Datta - Cupertino CA, US
Dorin Comaniciu - Princeton Junction NJ, US

Siemens Medical Solutions USA, Inc. - Malvern PA
Siemens Corporation - Iselin NJ

A61B 8/06

600454

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.

2012002, Jan 26, 2012

Apr 20, 2011

13/091076

Ingmar Voigt - Erlangen, DE
Viorel Mihalef - Keasbey NJ, US
Sasa Grbic - Erlangen, DE
Dime Vitanovski - Erlangen, DE
Yang Wang - Plainsboro NJ, US
Yefeng Zheng - Dayton NJ, US
Bogdan Georgescu - Plainsboro NJ, US
Dorin Comaniciu - Princeton Junction NJ, US
Puneet Sharma - Rahway NJ, US
Tommaso Mansi - Westfield NJ, US

G06G 7/60
G06G 7/57

703 9, 703 11

A method and system for patient-specific modeling of the whole heart anatomy, dynamics, hemodynamics, and fluid structure interaction from 4D medical image data is disclosed. The anatomy and dynamics of the heart are determined by estimating patient-specific parameters of a physiological model of the heart from the 4D medical image data for a patient. The patient-specific anatomy and dynamics are used as input to a 3D Navier-Stokes solver that derives realistic hemodynamics, constrained by the local anatomy, along the entire heart cycle. Fluid structure interactions are determined iteratively over the heart cycle by simulating the blood flow at a given time step and calculating the deformation of the heart structure based on the simulated blood flow, such that the deformation of the heart structure is used in the simulation of the blood flow at the next time step. The comprehensive patient-specific model of the heart representing anatomy, dynamics, hemodynamics, and fluid structure interaction can be used for non-invasive assessment and diagnosis of the heart, as well as virtual therapy planning and cardiovascular disease management. Parameters of the comprehensive patient-specific model are changed or perturbed to simulate various conditions or treatment options, and then the patient specific model is recalculated to predict the effect of the conditions or treatment options.

2012007, Mar 29, 2012

Sep 16, 2011

13/234697

Yang Wang - Plainsboro NJ, US
Bogdan Georgescu - Plainsboro NJ, US
Helene C. Houle - San Jose CA, US
Dorin Comaniciu - Princeton Junction NJ, US

Siemens Medical Solutions USA, Inc. - Malvern PA
Siemens Corporation - Iselin NJ

A61B 8/08

600437

Computerized characterization of cardiac wall motion is provided. Quantities for cardiac wall motion are determined from a four-dimensional (i.e., 3D+time) sequence of ultrasound data. A processor automatically processes the volume data to locate the cardiac wall through the sequence and calculate the quantity from the cardiac wall position or motion. Various machine learning is used for locating and tracking the cardiac wall, such as using a motion prior learned from training data for initially locating the cardiac wall and the motion prior, speckle tracking, boundary detection, and mass conservation cues for tracking with another machine learned classifier. Where the sequence extends over multiple cycles, the cycles are automatically divided for independent tracking of the cardiac wall. The cardiac wall from one cycle may be used to propagate to another cycle for initializing the tracking. Independent tracking in each cycle may reduce or avoid inaccuracies due to drift.

2012023, Sep 13, 2012

Mar 6, 2012

13/412869

Sasa Grbic - Erlangen, DE
Yang Wang - Princeton NJ, US
Bogdan Georgescu - Plainsboro NJ, US
Tommaso Mansi - Westfield NJ, US
Dorin Comaniciu - Princeton Junction NJ, US
Yefeng Zheng - Dayton NJ, US
Shaohua Kevin Zhou - Plainsboro NJ, US
Matthias John - Nurnberg, DE
Jan Boese - Eckental, DE

Siemens Aktiengesellschaft - Munich
Siemens Corporation - Iselin NJ

G06K 9/36

382131, 382128

A method and system for fusion of multi-modal volumetric images is disclosed. A first image acquired using a first imaging modality is received. A second image acquired using a second imaging modality is received. A model and of a target anatomical structure and a transformation are jointly estimated from the first and second images. The model represents a model of the target anatomical structure in the first image and the transformation projects a model of the target anatomical structure in the second image to the model in the first image. The first and second images can be fused based on estimated transformation.

2013014, Jun 6, 2013

Nov 20, 2012

13/682272

Yang Wang - Princeton NJ, US
Bogdan Georgescu - Plainsboro NJ, US
Saurabh Datta - Pleasanton CA, US

A61B 8/06

600438

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.