Hyperfine Operations, Inc.

Seyed Sadegh Mosheni Salehi

Michal Sofka

Jo Schlemper

Jonathan Marc Rothberg

Laura Sacolick

Hadrien A Dyvorne

Rafael O'Halloran

Carole Lazarus

Prantik Kundu

Ziyi Wang

A magnetic resonance imaging (MRI) system, comprising: a magnetics system comprising: a Bmagnet configured to provide a Bfield for the MRI system; gradient coils configured to provide gradient fields for the MRI system; and at least one RF coil configured to detect magnetic resonance (MR) signals; and a controller configured to: control the magnetics system to acquire MR spatial frequency data using non-Cartesian sampling; and generate an MR image from the acquired MR spatial frequency data using a neural network model comprising one or more neural network blocks including a first neural network block, wherein the first neural network block is configured to perform data consistency processing using a non-uniform Fourier transformation.

Deep learning techniques for magnetic resonance image reconstruction

Generating magnetic resonance (MR) images of a subject from MR data obtained by a magnetic resonance imaging (MRI) system by: generating first and second sets of one or more MR images from first and second input MR data; aligning the first and second sets of MR images using a neural network model comprising first and second neural networks, the aligning comprising: estimating, using the first neural network, a first transformation between the first and second sets of MR images; generating a first updated set of MR images from the second set of MR images using the first transformation; estimating, using the second neural network, a second transformation between the first set and the first updated set of MR images; and aligning the first set of MR images and the second set of MR images at least in part by using the first transformation and the second transformation.

Deep learning techniques for alignment of magnetic resonance images

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Bloomfield, NJ, United States of America

Seyed Sadegh Mosheni Salehi