Ge Precision Healthcare LLC

New York Society for the Relief of the Ruptured and Crippled, Maintaining the Hospital for Special Surgery

Kang Wang

Florian Wiesinger

Robert Marc Lebel

Xiaoli Zhao

Hua Li

Yan Wen

Ek Tsoon Tan

Darryl Brett Sneag

Ty A Cashen

Rolf Schulte

Mei Kei Maggie Fung

Zhenghui Zhang

A computer-implemented method for suppressing fat in reconstructed magnetic resonance imaging data includes generating, via a processor, a first echo image and a second echo image from magnetic resonance scan data acquired of a subject with a magnetic resonance scanner utilizing a multi-echo steady state sequence. The computer-implemented method also includes estimating, via the processor, an echo phase difference between the first echo image and the second echo image to generate an echo phase difference image. The computer-implemented method further includes identifying, via the processor, voxels in the echo phase difference image having an extra chemical shift phase. The computer-implemented method even further includes removing, via the processor, signals of the voxels identified having the extra chemical shift phase in both the first echo image and the second echo image to generate a first fat-removed image and a second fat-removed image, respectively.

System and method for multi-echo steady-state sequence with improved fat suppression using adapted Dixon technique

A computer-implemented method of reducing noise in magnetic resonance (MR) images is provided. The method includes executing a neural network model of analyzing MR images, wherein the neural network model is trained with a pair of pristine images and corrupted images. The pristine images are the corrupted images with noise reduced, and target output images of the neural network model are the pristine images. The method also includes receiving first MR signals and second MR signals, reconstructing first and second MR images based on the first MR signals and the second MR signals, and analyzing the first MR image and the second MR image using the neural network model. The method further includes deriving a denoised MR image based on the analysis, wherein the denoised MR image is a combined image based on the first MR image and the second MR image and outputting the denoised MR image.

Systems and methods of noise reduction in magnetic resonance images

A method for generating an image of an object with a magnetic resonance imaging (MM) system is presented. The method includes first performing a calibration scan of the object. The calibration scan is performed with a zero echo time (ZTE) radial sampling scheme to obtain calibration k-spaces for surface coil elements and a body coil of the MRI system. The calibration scan is performed in such a manner that the endpoints of calibration k-space lines in each calibration k-space follow a spiral path. A plurality of calibration parameters are then obtained from the plurality of calibration k-spaces. A second scan of the object is then performed to acquire the MR image data. The image of the object is then generated based on the plurality of calibration parameters and the MR image data.

Silent calibration for magnetic resonance imaging

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Sussex, WI, United States of America

Kang Wang