General Electric Company

Ge Precision Healthcare LLC

David Erik Chevalier

Eric Michael Gros

Fausto Espinal

Chelsey Amanda Lewis

Ravindra Mohan Manjeshwar

Bradley Jay Gabrielse

Sangtae Ahn

Michael Sarju Vaz

Steven Gerard Ross

Evren Asma

David Joseph Pitterle

Evgeny Drapkin

Christine Carol Hammond

Elizabeth Janus Nett

Michael Braunstein

Roshni Bhagalia

Nikhil Jones Tomy

Kenji Okabe

David Minor

Ben Dayan

Greg Ohme

Manoj Unnikrishnan

Khaleel Ahamad Nadaf

David Alexander Polyak

Medical scanner application platforms and associated components (e.g., using a computerized tool) is enabled. For example, a system, can comprise: a processor, and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations, comprising: determining, based on package schema data representative of a package schema of an application received via a network, one or more attributes of the application, based on the one or more attributes of the application, determining one or more constraints of the application applicable to a medical scanner communicatively coupled to the system via an agent of the medical scanner, and storing the application in an application database accessible to the agent of the medical scanner.

Medical scanner application platform

Techniques regarding the management of computational resources based on clinical priority associated with one or more computing tasks are provided. For example, one or more embodiments described herein can regard a system comprising a memory that can store computer-executable components. The system can also comprise a processor, operably coupled to the memory, that executes the computer-executable components stored in the memory. The computer-executable components can include a prioritization component that can prioritize computer applications based on a clinical priority of tasks performed by the computer applications. The clinical priority can characterize a time sensitivity of the tasks. The computer-executable components can also include a resource pool component that can divide computational resources across a plurality of resource pools and can assign the computer applications to the plurality of resource pools based on the clinical priority.

Managing computer resources for clinical applications

Methods and systems are provided for generating deep learning training data with an imaging system. In one embodiment, a method for an imaging system comprises performing a scan of a subject to acquire imaging data, training a deep neural network on the imaging data to obtain updates to the deep neural network, and transmitting the updates to a server for training a central deep neural network. In this way, imaging data may be leveraged for training and developing global deep learning models without transmitting the imaging data itself, thereby preserving patient privacy.

Systems and methods for capturing deep learning training data from imaging systems

Methods and systems are provided for capturing deep learning training data from imaging systems. In one embodiment, a method for an imaging system comprises performing a scan of a subject to acquire imaging data, inputting the imaging data to a deep neural network, displaying an output of the deep neural network and an image reconstructed from the imaging data, and transmitting an intermediate representation of the imaging data generated by the deep neural network to a server for training a central deep neural network. In this way, imaging data may be leveraged for training and developing global deep learning models without transmitting the imaging data itself, thereby preserving patient privacy.

Methods and systems are provided for adaptive scan control. In one embodiment, a method comprises: while performing a scan of a scan subject, processing acquired projection data to measure a contrast level; responsive to the contrast level increasing above a first threshold, automatically switching the scan from a first scan protocol to a second scan protocol; responsive to the contrast level decreasing below a second threshold, automatically switching the scan from the second scan protocol to the first scan protocol; and responsive to the contrast level decreasing below a third threshold, automatically ending the scan. In this way, multiple scan protocols, such as angiography and perfusion scan protocols, can be interleaved within a single scan without the use of a separate timing bolus scan.

Methods and systems for adaptive scan control

Embodiments of methods, systems and non-transitory computer readable media for tomographic imaging are presented. 3D TOF projection data is processed to generate corresponding data in a designated format that allows for computationally cheaper image reconstruction than the 3D TOF projection data. Further, one or more preliminary images are reconstructed from the processed data using a particular image reconstruction technique for one or more iterations. To that end, one or more imaging parameters are iteratively varied every one or more iterations. The imaging parameters, for example, include the designated format, the image reconstruction technique and one or more image quality characteristics. One or more intermediate images are reconstructed from the one or more preliminary images using the iteratively varying imaging parameters. Further, 3D TOF images having the one or more designated image quality characteristics are generated using the one or more intermediate images and the iteratively varying imaging parameters.

Methods and systems for enhanced tomographic imaging

Growing community of inventors

Menomonee Falls, WI, United States of America

David Erik Chevalier