Ge Precision Healthcare LLC

Justin M Wanek

Gireesha Chinthamani Rao

Ping Xue

German Guillermo Vera Gonzalez

Gopal Biligeri Avinash

Ravi Soni

Tao Tan

Pál Tegzes

Zita Herczeg

Fengchao Zhang

Michel S Tohme

Najib Akram

German Guillermo Vera-Gonzalez

Balázs P Cziria

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

A method for automatic selection of display settings for a medical image is provided. The method includes receiving a medical image, mapping the medical image to an appearance classification cell of an appearance classification matrix using a trained deep neural network, selecting a first WW and a first WC for the medical image based on the appearance classification and a target appearance classification, adjusting the first WW and the first WC based on user preferences to produce a second WW and a second WC, and displaying the medical image with the second WW and the second WC via a display device.

Systems and methods for consistent presentation of medical images using deep neural networks

Various methods and systems are provided for x-ray imaging. In one embodiment, a method comprises acquiring an image of a subject, generating, based on the image and a plurality of parameters, a noise modulation map comprising an estimated amount of noise in each pixel of the image, selectively reducing noise in the image based on the noise modulation map to generate a final image, and displaying the final image. In this way, the radiation dose during imaging may be reduced while maintaining or even improving image quality.

Methods and systems for parametric noise modulation in x-ray imaging

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Madison, WI, United States of America

Justin M Wanek