General Electric Company

Ge Medical Systems Global Technology Company, LLC

Ge Medical Systems Information Technologies Gmbh

Ge Precision Healthcare LLC

University of Washington

Ge Medical Systems

Saad Ahmed Sirohey

Gopal Biligeri Avinash

Jiang Hsieh

Sandeep Dutta

Paul Edgar Licato

Ananth P Mohan

Jerome F Knoplioch

Laurent Launay

Zhongmin Steve Lin

John Skinner

Tamanna Nanavaty Bembenek

Patricia Le Nezet

Deann Marie Haas

Renaud Capolunghi

Matthieu Denis Ferrant

Rahul Bhotika

Zhye Yin

Xin Wang

Bruno De Man

Cheryl Ruth Jones

David Charles Mack

Robert D Barnes

Toan Thanh Le

Kelly Lynn Karau

Fausto J Espinal

Rakesh Mohan Lal

Vincent Adam

Olga Imas

Peter Lehel

Peter Michael Edic

Darin Robert Okerlund

Robert F Senzig

Giridhar Jothiprasad

Steven James Gray

John Irvin Jackson

Paulo Ricardo Mendonca

Bijan Dorri

Ravikanth Avancha

Joseph Carroll

Beth Ann Heckel

Toshihiro Rifu

Basel Taha

Litao Yan

Maud Bonnard

Akira Hagiwara

Frederick Wilson Wheeler

Ricardo Scott Avila

Sidharth Abrol

Mark Vincent Profio

Bipul Das

Tin-Su Pan

Paulo Ricardo Dos Santos Mendonca

Roy A Nilsen

Prathyusha K Salla

Max Wintermark

Guangzhi Cao

Christopher Joseph Mussack

Mukta Chandrashekhar Joshi

Robert C Gemperline

Kelly Lynn Piacsek

Jérôme Francois Knoplioch

Istvan Ubelhart

Kelly Ann Mohr

Wesley David Turner

Fei Zhao

Karen Procknow

Allison Leigh Weiner

Satoshi Minoshima

Bob L Beckett

Steven M Zanoni

Sardar Gautham

Suresh Narayanan Narayanan

Balazs Cziria

Jan De Vaan

Huy-Nam Doan

Jérôme François Knoplioch

Srinivas Aluri

Tinsu Pan

Shiying Hu

Claire Naslot

Sudipta Mukhopadhyay

Guillaume Neveux

Jingbin Wang

Yannick Le Berre

Mirelle Audet

Thierry Galas

Ramakanth Vengala Ranga

Shibu Prabhakaran Pillai

Pradipto Kolay

Techniques are described for domain adaptation of image processing models using post-processing model correction According to an embodiment, a method comprises training, by a system operatively coupled to a processor, a post-processing model to correct an image-based inference output of a source image processing model that results from application of the source image processing model to a target image from a target domain that differs from a source domain, wherein the source image processing model was trained on source images from the source domain. In one or more implementations, the source imaging processing model comprises an organ segmentation model and the post-processing model can comprise a shape-autoencoder. The method further comprises applying, by the system, the source image processing model and the post-processing model to target images from the target domain to generate optimized image-based inference outputs for the target images.

Domain adaptation using post-processing model correction

A system and method for estimating vascular flow using CT imaging include a computer readable storage medium having stored thereon a computer program comprising instructions, which, when executed by a computer, cause the computer to acquire a first set of data comprising anatomical information of an imaging subject, the anatomical information comprises information of at least one vessel. The instructions further cause the computer to process the anatomical information to generate an image volume comprising the at least one vessel, generate hemodynamic information based on the image volume, and acquire a second set of data of the imaging subject. The computer is also caused to generate an image comprising the hemodynamic information in combination with a visualization based on the second set of data.

System and method for estimating vascular flow using CT imaging

Methods and apparatus for deep learning-based system design improvement are provided. An example system design engine apparatus includes a deep learning network (DLN) model associated with each component of a target system to be emulated, each DLN model to be trained using known input and known output, wherein the known input and known output simulate input and output of the associated component of the target system, and wherein each DLN model is connected as each associated component to be emulated is connected in the target system to form a digital model of the target system. The example apparatus also includes a model processor to simulate behavior of the target system and/or each component of the target system to be emulated using the digital model to generate a recommendation regarding a configuration of a component of the target system and/or a structure of the component of the target system.

Hardware system design improvement using deep learning algorithms

Methods and apparatus to automatically generate an image quality metric for an image are provided. An example method includes automatically processing a first medical image using a deployed learning network model to generate an image quality metric for the first medical image, the deployed learning network model generated from a digital learning and improvement factory including a training network, wherein the training network is tuned using a set of labeled reference medical images of a plurality of image types, and wherein a label associated with each of the labeled reference medical images indicates a central tendency metric associated with image quality of the image. The example method includes computing the image quality metric associated with the first medical image using the deployed learning network model by leveraging labels and associated central tendency metrics to determine the associated image quality metric for the first medical image.

Deep learning medical systems and methods for image reconstruction and quality evaluation

Methods and apparatus for improved deep learning for image acquisition are provided. An imaging system configuration apparatus includes a training learning device including a first processor to implement a first deep learning network (DLN) to learn a first set of imaging system configuration parameters based on a first set of inputs from a plurality of prior image acquisitions to configure at least one imaging system for image acquisition, the training learning device to receive and process feedback including operational data from the plurality of image acquisitions by the at least one imaging system. The example apparatus includes a deployed learning device including a second processor to implement a second DLN, the second DLN generated from the first DLN of the training learning device, the deployed learning device configured to provide a second imaging system configuration parameter to the imaging system in response to receiving a second input for image acquisition.

Deep learning medical systems and methods for image acquisition

An imaging system includes an imaging unit, a display unit, and at least one processor. The at least one processor is configured to acquire a first type of diagnostic imaging information of the patient; reconstruct a first image using the first type of diagnostic imaging information; if a first stop criterion for terminating imaging is not satisfied, acquire a second type of diagnostic imaging information having an increased level of acquisitional burden; reconstruct a second image; if a second stop criterion for terminating imaging is not satisfied, acquire a third type of diagnostic imaging information having an increased level of acquisitional burden, wherein the patient is maintained on a table of the imaging unit during the acquisition of the second type of diagnostic imaging information, reconstruction of the second image, and acquisition of the third type of diagnostic imaging information; reconstruct a third image; and display the third image.

Systems and methods for progressive imaging

Methods and apparatus for monitoring and improving imaging system operation are provided. An example apparatus includes a first deployed deep learning network (DLN) which operates with an acquisition engine to generate an imaging device configuration. The example apparatus includes a second deployed DLN which operates with a reconstruction engine based on acquired image data. The example apparatus includes a first assessment engine with a third deployed DLN. The assessment engine receives output from at least one of the acquisition engine or the reconstruction engine to assess operation of the respective at least one of the acquisition engine or the reconstruction engine and to provide feedback to the respective at least one of the acquisition engine or the reconstruction engine. The first deployed DLN and the second deployed DLN are generated and deployed from first and second training DLNS, respectively.

Deep learning medical systems and methods for medical procedures

A computed tomography (CT) imaging system includes a CT imaging unit, a display unit and at least one processor. The CT imaging unit includes an X-ray source and a CT detector. The at least one processor is operably coupled to the imaging unit and the display unit, and is configured to: acquire at least three phases of CT imaging information via the CT imaging unit; determine timing information for imaging intensity of blood vessels represented in the CT imaging information; assign corresponding colors to the blood vessels based on the timing information; reconstruct an image using the CT imaging information from the at least three phases, wherein the blood vessels depicted in the reconstructed image are represented using the corresponding colors based on the timing information; and display the image on the display unit.

Systems and methods for color visualization of CT images

An imaging system includes a computed tomography (CT) acquisition unit and a processing unit. The CT acquisition unit includes an X-ray source and a CT detector configured to collect CT imaging data of an object to be imaged. The processing unit includes at least one processor operably coupled to the CT acquisition unit. The processing unit is configured to control the CT acquisition unit to collect at least one sample projection during rotation of the CT acquisition unit about the object to be imaged, compare an intensity of the at least one sample projection to an intensity of a reference projection, select a time to perform an imaging scan based on the comparison of the intensity of the at least one sample projection to the intensity of the reference projection, and control the CT acquisition unit to perform the imaging scan.

Systems and methods for imaging phase selection for computed tomography imaging

Growing community of inventors

Waukesha, WI, United States of America

Saad Ahmed Sirohey