Ge Precision Healthcare LLC

General Electric Company

Ge Medical Systems Global Technology Company, LLC

Gireesha Chinthamani Rao

Katelyn Rose Nye

Gopal Biligeri Avinash

Ravi Soni

Tao Tan

Alec Joseph Baenen

Khaled Salem Younis

Lehel Ferenczi

German Guillermo Vera Gonzalez

Teri Lynn Fischer

James Zhengshe Liu

John Michael Sabol

Pál Tegzes

Dejun Wang

Buer Qi

Levente Torok

Poonam Dalal

Kenneth Scott Kump

Soumya Ghose

Yaan Ge

Chuande Liu

Zita Herczeg

László Ruskó

Nasir Ahmed Desai

Beth Ann Heckel

Levente Imre Török

Justin M Wanek

Hongxu Yang

Najib Akram

Yash N Shah

John Eric Tkaczyk

Christopher B Austin

Jie Liu

Rowland F Saunders

Ping Xue

Sylvain Bernard

Vincent Jonas Bismuth

Feng Xu

Jon Charles Omernick

Cheryl Ruth Jones

Manfred David Boehm

Medhi Venon

Tabb A Patz

Zoltan Kiss

Raghu Prasad

Dibyajyoti Pati

Xianfeng Ni

Fusheng Li

Huanzhong Li

Robert A Buchanan

Srikrishnan Viswanathan

Arun Kumar Chandrashekarappa

Kristine Gould

Chunyu Wang

Chen Liu

Jingyi Yang

Prabhu Rajasekaran

Lehel Mihály Ferenczi

Fengchao Zhang

Yu Qing Li

Eric Hart

Adam Pluim

Balazs Peter Cziria

Qingming Peng

Qingtao Wang

Aizhen Zhou

Scott R Wiese

Srinivas Aluri

Srikrishnan V

Pulak Goswami

Najib Akram Maheen Aboobacker

Jessica Tsai Wen

Ariel Seitz Kniss

Aishwarya Seth

Balázs P Cziria

Xiaomeng Dong

Scott Schubert

Joshua Imobersteg

Keval Harishabhai Jagatiya

Dennis Wei Zhou

German Guillermo Vera-Gonzalez

Michael Slavkin

Manu Ramesh

Benjamin James Beeman

Francisco Ruiz Gomez

Craig Dennis

Kiran Shankaranarayana

Karla Angelica Arista

Molin Zhang

Systems/techniques that facilitate improved deep learning image processing are provided. In various embodiments, a system can access a medical image, wherein pixels or voxels of the medical image can be allocated among a plurality of regions. In various aspects, the system can generate, via execution of a deep learning neural network on the medical image, a set of region-wise parameter maps, wherein a region-wise parameter map can consist of one predicted parameter per region of the medical image. In various instances, the system can generate a transformed version of the medical image by feeding the set of region-wise parameter maps to an analytical transformation function. In various cases, the system can render the transformed version of the medical image on an electronic display. In various aspects, the plurality of regions can be irregular or tissue-based.

Deep learning image processing via region-wise parameter maps

Display screen or portion thereof with a computer generated graphical user interface for placement of a medical tube or line

An artificial intelligence (AI) lead marker detection system is employed either as a component of the X-ray imaging system or separately from the X-ray imaging system to scan post-exposure X-ray images to detect and insert various lead markers, to digitize information provided by the type and location of the lead marker, and to employ the marker information in different X-ray system workflow automations. The marker information obtained by the AI lead marker detection system can also provide useful data for use in downstream clinical and quality applications apart from the X-ray system, such as either AI or non-AI analytical applications.

X-ray lead marker detection system for x-ray imaging system

An image processing system is provided. The image processing system includes a display, a processor, and a memory. The memory stores processor-executable code that when executed by the processor causes receiving images of a region of interest of a patient with an enteric tube or line disposed within the region of interest, detecting the medical tube or line within the image, generating a combined image by combining the received images, and superimposing graphical markers on the combined image that indicate placement or misplacement of the enteric tube or line, and displaying the combined image on a display. In further aspects, a classification of the enteric tube or line (e.g., correctly placed tube present, malpositioned tube present, and so forth) and a detected positional change in the placement of the enteric tube or line may be determined and communicated to one or more clinicians. Additionally, the outputs of the image processing system may also be provided to facilitate triage of patients, helping prioritize which tube placements require further attention and in what order.

System and method for visualizing placement of a medical tube or line

A dual energy x-ray imaging system and method of operation includes an artificial intelligence-based motion correction system to minimize the effects of motion artifacts in images produced by the imaging system. The motion correction system is trained to apply simulated motion to various objects of interest within the LE and HE projections in the training dataset to improve registration of the LE and HE projections. The motion correction system is also trained to enhance the correction of small motion artifacts using noise attenuation and subtraction image-based edge detection on the training dataset images reduce noise from the LE projection, consequently improving small motion artifact correction. The motion correction system additionally employs separate motion corrections for soft and bone tissue in forming subtraction soft tissue and bone tissue images, and includes a motion alarm to indicate when motion between LE and HE projections requires a retake of the projections.

Artificial intelligence-based dual energy x-ray image motion correction training method and system

An artificial intelligence (AI) X-ray image information detection and correction system is employed either as a component of the X-ray imaging system or separately from the X-ray imaging system to automatically scan post-exposure X-ray images to detect various types of information or characteristics of the X-ray image, including, but not limited to, anatomy, view, orientation and laterality of the X-ray image, along with an anatomical landmark segmentation. The information detected about the X-ray image can then be stored by the AI system in association with the X-ray image for use in various downstream X-ray system workflow automations and/or reviews of the X-ray image.

System and method for automatic detection and correction of anatomy, view, orientation and laterality in X-ray image for X-ray imaging system

An image processing system is provided. The image processing system includes a display, a processor, and a memory. The memory stores processor-executable code that when executed by the processor causes receiving an image of a region of interest of a patient with a medical tube or line disposed within the region of interest, detecting the medical tube or line within the image, generating a combined image by superimposing a first graphical marker on the image that indicates an end of the medical tube or line, and displaying the combined image on the display.

A tomosynthesis machine allows for faster image acquisition and improved signal-to-noise by acquiring a projection attenuation data and using machine learning to identify a subset of the projection attenuation data for the production of thinner slices and/or higher resolution slices using machine learning.

X-ray tomosynthesis system providing neural-net guided resolution enhancement and thinner slice generation

Techniques are described for optimizing deep learning model performance using image harmonization as a pre-processing step. According to an embodiment, a method comprises decomposing, by a system operatively coupled to a processor, an input image into sub-images. The method further comprises harmonizing the sub-images with corresponding reference sub-images of at least one reference image based on two or more different statistical values respectively calculated for the sub-images and the corresponding reference-sub images, resulting in transformation of the sub-images into modified sub-images images. In some implementations, the modified sub-images can be combined into a harmonized image having a more similar appearance to the at least one reference image relative to the input image. In other implementations, harmonized images and/or modified sub-images generated using these techniques can be used as ground-truth training samples for training one or more deep learning model to transform input images with appearance variations into harmonized images.

Image harmonization for deep learning model optimization

The current disclosure provides methods and systems for rapidly and consistently determining medical image quality metrics following acquisition of a diagnostic medical image. In one embodiment, the current disclosure teaches a method for determining an image quality metric by, acquiring a medical image of an anatomical region, mapping the medical image to a positional attribute of an anatomical feature using a trained deep neural network, determining an image quality metric based on the positional attribute of the anatomical feature, determining if the image quality metric satisfies an image quality criterion, and displaying the medical image, the image quality metric, and a status of the image quality criterion via a display device. In this way, a diagnostic scanning procedure may be expedited by providing technicians with real-time insight into quantitative image quality metrics.

System and methods for medical image quality assessment using deep neural networks

Methods and systems are provided for tuning a static model with multiple operating points to adjust model performance without retraining the model or triggering a new regulatory clearance. In one embodiment, a method comprises, responsive to a request to tune a model, obtaining a tuning dataset including a set of medical images, executing the model using the set of medical images as input to generate model tuning output, and determining, for each operating point of a set of operating points, a set of tuning metric values based on the tuning dataset and the model tuning output relative to each operating point. An operating point from the set of operating points may be selected based on each set of tuning metric values and, upon a request to analyze a subsequent medical image, a representation of a finding output from the static model executed at the selected operating point.

Methods and systems for tuning a static model

Systems/techniques that facilitate AI-based region-of-interest masks for improved data reconstructions are provided. In various embodiments, a system can access a set of two-dimensional medical scan projections. In various aspects, the system can generate a set of two-dimensional region-of-interest masks respectively corresponding to the set of two-dimensional medical scan projections. In various instances, the system can generate a region-of-interest visualization based on the set of two-dimensional region-of-interest masks and the set of two-dimensional medical scan projections. In various cases, the system can generate the set of two-dimensional region-of-interest masks by executing a machine learning segmentation model on the set of two-dimensional medical scan projections.

AI-based region-of-interest masks for improved data reconstruction

An X-ray system with a universal positioning system includes a multiple degree of freedom overhead support system mounted within a location for the X-ray system, a first imaging device on the overhead support system, a multiple degree of freedom wall stand disposed within the location for the X-ray system, the wall stand comprising a motive module and a number of moveable members operably connected to the motive module that can be operated by the motive module to move the wall stand over a floor of the location, a second imaging device mounted to the wall stand, a table disposed within the location for the X-ray system, including a base disposed on the floor of the location and a support surface secured at one end to the base, and a workstation including a processing unit configured to send control signals to and to receive data signals from the universal positioning system.

Universal positioning system for X-ray imaging system

An x-ray image laterality detection system is provided. The x-ray image laterality detection system includes a detection computing device. The processor of the computing device is programmed to execute a neural network model for analyzing x-ray images, wherein the neural network model is trained with training x-ray images as inputs and observed laterality classes associated with the training x-ray images as outputs. The process is also programmed to receive an unclassified x-ray image, analyze the unclassified x-ray image using the neural network model, and assign a laterality class to the unclassified x-ray image. If the assigned laterality class is not target laterality, the processor is programmed to adjust the unclassified x-ray image to derive a corrected x-ray image having the target laterality and output the corrected x-ray image. If the assigned laterality class is the target laterality, the processor is programmed to output the unclassified x-ray image.

Systems and methods for detecting laterality of a medical image

Apparatus, systems, and methods to deliver point of care alerts for radiological findings are disclosed. An example imaging apparatus includes an image data store, an image quality checker, and a training learning network. The example image data store is to store image data acquired using the imaging apparatus. The example image quality checker is to evaluate image data from the image data store in comparison to an image quality measure. The example trained learning network is to process the image data to identify a clinical finding in the image data, the identification of a clinical finding to trigger a notification at the imaging apparatus to notify a healthcare practitioner regarding the clinical finding and prompt a responsive action with respect to a patient associated with the image data.

Systems and methods to deliver point of care alerts for radiological findings

Growing community of inventors

Pewaukee, WI, United States of America

Gireesha Chinthamani Rao