Leland Stanford Junior University

The United States Government As Represented by the Department of Veterans Affairs

Julio A Oscanoa Aida

Shreyas S Vasanawala

Frank Ong

Daniel B Ennis

Matthew J Middione

Mert Pilanci

In a method for magnetic resonance imaging pseudorandomly undersampled k-space imaging data is acquired with multiple receiver coils of an MRI imaging apparatus. MR image reconstruction is performed to produce a reconstructed MR image from the k-space imaging data by iteratively solving sketched approximations of an original reconstruction problem. The sketched approximations use a sketched model matrix Athat is a lower-dimensional version of an original model matrix A of the original reconstruction problem. The sketched model matrix Apreserves the Fourier structure of the MR reconstruction problem and reduces the number of coils actively used during reconstruction.

Randomized dimension reduction for magnetic resonance image iterative reconstruction

A method for phase-contrast magnetic resonance imaging (PC-MRI) acquires undersampled PC-MRI data using a magnetic resonance imaging scanner and reconstructs MRI images from the undersampled PC-MRI data by reconstructing a first flow-encoded image using a first convolutional neural network, reconstructing a complex difference image using a second convolutional neural network, combining the complex difference image and the first flow-encoded image to obtain a second flow-encoded image, and generating a velocity encoded image from the first flow-encoded image and second flow-encoded image using phase difference processing.

Artificial intelligence based reconstruction for phase contrast magnetic resonance imaging

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Stanford, CA, United States of America

Julio A Oscanoa Aida