General Electric Company

Ge Precision Healthcare LLC

Prem Venugopal

Peter Michael Edic

Jed Douglas Pack

Soumya Ghose

Jhimli Mitra

Cynthia Elizabeth Landberg Davis

Robert F Senzig

Paul Hadley Vitt

David Allen Langan

Kai Erik Thomenius

Brian Keith

Steven James Gray

Thomas William Vandeputte

Jeffrey Brian Fowlkes

Jiang Hsieh

Christopher Judson Hardy

Bruno Kristiaan Bernard De Man

Thomas Kwok-Fah Foo

James Vradenburg Miller

Brian Chandler Barr

Xin Wang

Ek Tsoon Tan

Sylvain Pierre

Alexander Stein

Jalindar Appa Walunj

Shourya Prakash Otta

Gregory John Kajfasz

Matthew Brian Surprenant

Xia Li

Ambady Suresh

Robert Franklin Senzig

A gas turbine engine includes a turbomachine having a compressor section, a combustion section, and a turbine section. The turbine section includes a band having an upstream end and a downstream end. The band extends between the upstream end and the downstream end, and the band at least partially defines the working gas flow path. A plurality of airfoils extend into the working gas flow path from the band. Each airfoil of the plurality of airfoils includes a leading edge, a trailing edge, a first side, and a second side opposite the first side. Each of the plurality of airfoils is substantially symmetric across an airfoil centerline extending through a center of each of the plurality of airfoils. The band defines a valley portion adjacent the leading edge of each of the plurality of airfoils and a pair of hill portions on opposing sides of the valley portion.

Gas turbine engine with bow wave mitigation

A method includes identifying stenosed region within vessel in vascular image data and generating a revascularized model of the vessel based on the vascular image data with a lumen boundary in the stenosed region adjusted to have a same cross-sectional area as healthy sections of the vessel. The method includes determining a first pressure distribution for the revascularized model at hyperemic flow, determining a second pressure distribution for the vessel in the vascular image data, and calculating a subtracted pressure distribution by subtracting the second pressure distribution from the first pressure distribution. The method includes determining an asymptotic value for the subtracted pressure distribution and calculating a value by subtracting the asymptotic value from a pressure value obtained from the second pressure distribution at a location at a beginning of the stenosed region. The method includes normalizing the value to obtain a location independent FFR value for the stenosed region.

System and method for location insensitive reporting of fractional flow reserve-computed tomography for a given stenosis

A computer-implemented method includes obtaining, via a processor, clinical images including vessels and generating, via the processor, straightened-out images for each coronary tree path within respective clinical images, The method also includes extracting, via the processor, segmented 3D image patches, determining, via the processor, overlapping binary mask volumes for each segment, and predicting, via the processor, pressure drops across the segmented image patches using a trained deep neural network.

System and method for deep-learning based estimation of coronary artery pressure drop

A computer-implemented method includes obtaining, via a processor, segmented image patches of a vessel along a coronary tree path and associated coronary flow distribution for respective vessel segments in the segmented image patches. The method also includes determining, via the processor, a pressure drop distribution along an axial length of the vessel from the segmented image patches and the associated coronary flow distribution. The method further includes determining, via the processor, critical points in the pressure drop distribution. The method even further includes detecting, via the processor, a presence of a stenosis based on the critical points in the pressure drop distribution.

System and method for detecting stenosis

A computer-implemented method includes generating, via a processor, synthetic vessels. The method also includes performing, via the processor, three-dimensional (3D) computational fluid dynamics (CFD) on the synthetic vessels for different flow rates to generate 3D CFD data. The method further includes extracting, via the processor, 3D image patches from the synthetic vessels. The method even further includes obtaining, via the processor, pressure drops across the 3D image patches from the 3D CFD data. The method yet further includes training, via the processor, a deep neural network utilizing the 3D image patches, the pressure drops, and associated flow rates to generate a trained deep neural network.

Systems and techniques that facilitate hybrid unsupervised and supervised image segmentation are provided. In various embodiments, a system can access a computed tomography (CT) image depicting an anatomical structure. In various aspects, the system can generate, via an unsupervised modeling technique, at least one class probability mask of the anatomical structure based on the CT image. In various instances, the system can generate, via a deep-learning model, an image segmentation based on the CT image and based on the at least one class probability mask.

Hybrid unsupervised and supervised image segmentation model

A turbine engine stage includes a plurality of airfoils extending between an inner band and an outer band. Each airfoil in the plurality of airfoils can have an outer wall defining a pressure side and a suction side, with the outer wall extending between a leading edge and a trailing edge. An intervening flow passage is defined between two adjacent airfoils in the plurality of airfoils.

Airfoil assembly with flow surface

A turbine rotor blade including an airfoil that extends from a platform. The platform may include a first portion of a nominal platform contour substantially in accordance with Cartesian coordinate values of X', Y&#x2032;, and Z&#x2032; as set forth in Table II. The Cartesian coordinate values of X&#x2032;, Y&#x2032;, and Z&#x2032; are non-dimensional values from 0% to 100% convertible to dimensional distances by multiplying the Cartesian coordinate values of X&#x2032;, Y&#x2032;, and Z&#x2032; by a height of the airfoil defined along a Z&#x2032; axis. The X&#x2032; and Y&#x2032; values of the first portion are coordinate values that, when connected by smooth continuing arcs, define contour lines of the first portion of the nominal airfoil profile at each Z&#x2032; coordinate value. The contour lines may be joined smoothly with one another to form the first portion.

Airfoil shape and platform contour for turbine rotor blades

The present disclosure relates to training one or more neural networks for vascular vessel assessment using synthetic image data for which ground-truth data is known. In certain implementations, the synthetic image data may be based in part, or derived from, clinical image data for which ground-truth data is not known or available. Neural networks trained in this manner may be used to perform one or more of vessel segmentation, decalcification, Hounsfield unit scoring, and/or estimation of a hemodynamic parameter.

Deep learning for arterial analysis and assessment

The present approach provides a non-invasive methodology for estimation of coronary flow and/or fractional flow reserve. In certain implementations, various approaches for personalizing blood flow models of the coronary vasculature are described. The described personalization approaches involve patient-specific measurements and do not assume or rely on the resting coronary flow being proportional to myocardial mass. Consequently, there are fewer limitations in using these approaches to obtain coronary flow and/or fractional flow reserve estimates non-invasively.

Methods for personalizing blood flow models

A method for determining an arterial pulse wave velocity representative of a health condition of a blood vessel includes receiving an image data set comprising a plurality of images of a subject, from an imaging modality. The method also involves determining a blood vessel region in an image from the plurality of images. The method further includes determining a plurality of cross-sectional area values of a blood vessel at a plurality of locations in the blood vessel region, corresponding to a plurality of phases of a cardiac cycle of the subject and determining a plurality of flow rate values of blood flowing in the blood vessel corresponding to the plurality of cross-sectional area values. The method also includes determining a hemodynamic model based on the plurality of cross-sectional area values and the plurality of blood flow rate values and determining the arterial pulse wave velocity based on the hemodynamic model.

System and method for estimating arterial pulse wave velocity

A turbine stage includes a row of airfoils joined to corresponding platforms to define flow passages therebetween. Each airfoil includes opposite pressure and suction sides and extends in chord between opposite leading and trailing edges. Each platform has a contoured flow surface including a purge valley, an elevated bulge and a bowl. The purge valley has a maximum depth at an elevation equal to or greater than a nominal axisymmetric platform surface of the corresponding platform. The purge valley extending tangentially against a purge cavity wall and into the blend area and extending axially from proximate the leading edge of a first airfoil toward the suction side of the first airfoil, and toward the leading edge of a second adjacent airfoil to channel a purge flow. The elevated bulge adjoins the pressure side aft of the leading edge and the bowl adjoins the suction side aft of the leading edge.

End wall contour for an axial flow turbine stage

Approaches for assessing hemodynamic characteristics for an organ of interest are related. In one implementation, a fluid dynamics model may be provided with data derived from an anatomic imaging modality and blood flow information derived by ultrasound to derive the desired hemodynamic characteristics. In one such implementation, a fractional flow reserve is estimated.

Fractional flow reserve estimation

A turbine stage includes a row of airfoils joined to corresponding platforms to define flow passages therebetween. Each airfoil includes opposite pressure and suction sides and extends in chord between opposite leading and trailing edges. Each platform has a scalloped flow surface including a purge trough commencing tangentially in a blend area of the platform. The purge trough extending axially toward the suction side of the airfoil, aft of the leading edge, to channel a purge flow.

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Clifton Park, NY, United States of America

Prem Venugopal