Ge Precision Healthcare LLC

University of Texas System

Xinzeng Wang

Robert Marc Lebel

Ersin Bayram

Daniel Vance Litwiller

Graeme Colin McKinnon

Arnaud Guidon

Ananth J Madhuranthakam

Sagar Mandava

Ali Ersoz

Tim Sprenger

Ivan Pedrosa

Robert E Lenkinski

Daniel Vanc Litwiller

A method for producing an image of a subject with a magnetic resonance imaging (MRI) comprises acquiring a first set of partial k-space data from the subject and generating a phase corrected image based on a phase correction factor and the first set of the partial k-space data. The method further includes transforming the phase corrected image into a second set of partial k-space data and reconstructing the image of the subject from the second set of the partial k-space data and a weighting function.

System and method for reconstruction using a high-resolution phase in magnetic resonance images

Methods and systems are provided for independently removing streak artifacts and noise from medical images, using trained deep neural networks. In one embodiment, streak artifacts and noise may be selectively and independently removed from a medical image by receiving the medical image comprising streak artifacts and noise, mapping the medical image to a streak residual and a noise residual using the trained deep neural network, subtracting the streak residual from the medical image to a first extent, and subtracting the noise residual from the medical image to a second extent, to produce a de-noised medical image, and displaying the de-noised medical image via a display device.

Methods and system for selective removal of streak artifacts and noise from images using deep neural networks

A computer-implemented method of removing truncation artifacts in magnetic resonance (MR) images is provided. The method includes receiving a crude image that is based on partial k-space data from a partial k-space that is asymmetrically truncated in at least one k-space dimension. The method also includes analyzing the crude image using a neural network model trained with a pair of pristine images and corrupted images. The corrupted images are based on partial k-space data from partial k-spaces truncated in one or more partial sampling patterns. The pristine images are based on full k-space data corresponding to the partial k-space data of the corrupted images, and target output images of the neural network model are the pristine images. The method further includes deriving an improved image of the crude image based on the analysis, wherein the derived improved image includes reduced truncation artifacts and increased high spatial frequency data.

Deep learning systems and methods of removal of truncation artifacts in magnetic resonance images

The present invention includes a method and apparatus for improved magnetic resonance imaging with simultaneous fat and fluid suppression of a subject comprising: acquiring four images, in-phase (IP) and out-of-phase (OP) at a short and a long echo time (TE) using a single-shot turbo spin echo from one or more magnetic resonance imager excitations: processing at least a pair of IP and OP images at a short and a long TE using single-shot turbo spin echo using a Dixon reconstruction; processing the pair of IP and OP images; subtracting the long TE water-only image from the shared-field-map Dixon reconstruction from the short TE water-only image to provide a fluid attenuation; processing water-only and fat-only images at the short and long TE to generate quantitative fat-fraction map; and reconstructing one or more 3D magnetic resonance images.

System and method for fast T2-weighted MR imaging with fat and fluid suppression

Methods and systems are provided for de-noising medical images using deep neural networks. In one embodiment, a method comprises receiving a medical image acquired by an imaging system, wherein the medical image comprises colored noise; mapping the medical image to a de-noised medical image using a trained convolutional neural network (CNN); and displaying the de-noised medical image via a display device. The deep neural network may thereby reduce colored noise in the acquired noisy medical image, increasing a clarity and diagnostic quality of the image.

Systems and methods for reducing colored noise in medical images using deep neural network

A computer-implemented method of correcting phase and reducing noise in magnetic resonance (MR) phase images is provided. The method includes executing a neural network model for analyzing MR images, wherein the neural network model is trained with a pair of pristine images and corrupted images, wherein the corrupted images include corrupted phase information, the pristine images are the corrupted images with the corrupted phase information reduced, and target output images of the neural network model are the pristine images. The method further includes receiving MR images including corrupted phase information, and analyzing the received MR images using the neural network model. The method also includes deriving pristine phase images of the received MR images based on the analysis, wherein the derived pristine phase images include reduced corrupted phase information, compared to the received MR images, and outputting MR images based on the derived pristine phase images.

Systems and methods of generating robust phase images in magnetic resonance images

The present invention includes a computerized method of detecting fluid flow in a vessel, the method comprising: obtaining at least one non-contrast enhanced magnetic resonance image from a magnetic resonance imager; performing a phase sensitive reconstruction of the at least one non-contrast enhanced magnetic resonance image using a processor; combining the phase sensitive reconstruction with a velocity selective preparation of the non-contrast enhanced magnetic resonance image, to determine using the processor, in a single acquisition, at least one of: a flow direction of a fluid in the vessel, a reduction or elimination of a background signal, body fat, water/fat separation, or differentiation of a fast moving flow signal from a slow moving flow signal in an opposite direction with suppression of the background signal; and storing or displaying at least one of flow direction or flow strength of the fluid flow in the vessel obtained from the single acquisition.

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Houston, TX, United States of America

Xinzeng Wang