General Electric Company

Ge Precision Healthcare LLC

Purdue Research Foundation

University of Notre Dame Du Lac

Rensselaer Polytechnic Institute

Jie Tang

Brian Edward Nett

Jed Douglas Pack

Jiang Hsieh

Charles Addison Bouman

Ken David Sauer

Zhye Yin

Ge Wang

Bruno Kristiaan Bernard De Man

Jean-Baptiste Thibault

Jiahua Fan

Lin Fu

Debashish Pal

Roy A Nilsen

Sathish Ramani

Hewei Gao

Eric Michael Gros

Ruoqiao Zhang

Roman Melnyk

Obaidullah Rahman

Madhuri Mahendra Nagare

Eric Gros

Roy Nilsen

Noise preserving models and methods for resolution recovery of x-ray computed tomography (e.g., using a computerized tool) are enabled. For example, a system can comprise: a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise: a pair generation component that generates a pair of images, the pair of images comprising an input image and a ground truth image, a training component that trains a machine learning based sharpening algorithm by approximately minimizing a loss function that determines an error between a sharpened image and the ground truth image, and a sharpening component that, using the sharpening algorithm, sharpens the input image to generate the sharpened image, wherein the sharpened image comprises a second noise that is similar in intensity to a first noise of the input image.

Noise preserving models and methods for resolution recovery of x-ray computed tomography images

Systems and methods are provided for computed tomography (CT) imaging. In one embodiment, a method comprises adaptively blending at least two input image volumes with different spatially-variant noise characteristics to generate an output image volume with uniform noise throughout the output image volume. In this way, images may be reconstructed from projection data with data redundancy without introducing image artifacts from stitching images or variance in image noise due to the data redundancy.

Systems and methods for adaptive blending in computed tomography imaging

The present disclosure relates to image reconstruction with favorable properties in terms of noise reduction, spatial resolution, detail preservation and computational complexity. The disclosed techniques may include some or all of: a first-pass reconstruction, a simplified datafit term, and/or a deep learning denoiser. In various implementations, the disclosed technique is portable to different CT platforms, such as by incorporating a first-pass reconstruction step.

Iterative image reconstruction framework

Systems and methods for improved image denoising using a deep learning network model are disclosed. An example system includes an input data processor to process a first patient image of a first patient to add a first noise to the first patient image to form a noisy image input. The example system includes an image data denoiser to process the noisy image input using a first deep learning network to identify the first noise. The image data denoiser is to train the first deep learning network using the noisy image input. When the first deep learning network is trained to identify the first noise, the image data denoiser is to deploy the first deep learning network as a second deep learning network model to be applied to a second patient image of the first patient to identify a second noise in the second patient image.

Patient-specific deep learning image denoising methods and systems

Various methods and systems for weighting material density images based on the material imaged are disclosed. In one embodiment, a method for dual energy imaging of a material comprises generating an odd material density image, generating an even material density image, applying a first weight to the odd material density image and a second weight to the even material density image, and generating a material density image based on a combination of the weighted odd material density image and the weighted even material density image. In this way, the image quality may be improved without increasing a radiation dosage.

Methods and systems for task-based data generation and weighting for CT spectral imaging

A method includes obtaining spectral computed tomography (CT) information via an acquisition unit having an X-ray source and a CT detector. The method also includes, generating, with one or more processing units, using at least one image transform, a first basis image and a second basis image using the spectral CT information. Further, the method includes performing, with the one or more processing units, guided processing on the second basis image using the first basis image as a guide to provide a modified second basis image. Also, the method includes performing at least one inverse image transform using the first basis image and the modified second basis image to generate at least one modified image.

Systems and methods for guided de-noising for computed tomography

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Waukesha, WI, United States of America

Jie Tang