Leland Stanford Junior University

Construction Research & Technology Gmbh

Frank Ong

Shreyas S Vasanawala

John Mark Pauly

Jeffrey R Bury

Samy M Shendy

Rick Paponetti

Christopher Michael Sandino

Raymond Fete

Julio A Oscanoa Aida

Mert Pilanci

Elizabeth K Cole

Thomas M Vickers

James Curtis Smith

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 MR imaging includes acquiring with an MR imaging apparatus undersampled k-space imaging data having one or more temporal dimensions and two or more spatial dimensions; transforming the undersampled k-space imaging data to image space data using zero-filled or sliding window reconstruction and sensitivity maps; decomposing the image space data into a compressed representation comprising a product of spatial and temporal parts, where the spatial part comprises spatial basis functions and the temporal part comprises temporal basis functions; processing the spatial basis functions and temporal basis functions to produce reconstructed spatial basis functions and reconstructed temporal basis functions, wherein the processing iteratively applies conjugate gradient and convolutional neural network updates using 2D or 3D spatial and 1D temporal networks; and decompressing the reconstructed spatial basis functions and reconstructed temporal basis functions to produce a reconstructed MRI image having one or more temporal dimensions and two or more spatial dimensions.

Method for high-dimensional image reconstruction using low-dimensional representations and deep learning

A method of magnetic resonance imaging acquires undersampled MRI data and generates by an adversarially trained generative neural network MRI data having higher quality without using any fully-sampled data as a ground truth. The generative neural network is adversarially trained using a discriminative neural network that distinguishes between undersampled MRI training data and candidate undersampled MRI training data produced by applying an MRI measurement function containing an undersampling mask to generated MRI training data produced by the generative neural network from the undersampled MRI training data.

MRI image reconstruction from undersampled data using adversarially trained generative neural network

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Los Altos, CA, United States of America

Frank Ong