Hyperfine Operations, Inc.

Duke University

Ziyi Wang

Jonathan Marc Rothberg

Michal Sofka

Laura Sacolick

Hadrien A Dyvorne

Rafael O'Halloran

Carole Lazarus

Jo Schlemper

Prantik Kundu

Bastiaan Driehuys

Seyed Sadegh Mohseni Salehi

Seyed Sadegh Moshen Salehi

Seyed Sadegh Mosheni Salehi

Sudarshan Rajagopal

Elianna Bier

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

Methods and systems withXe dynamic spectroscopy with a fitting function that includes one or more non-Lorentzians, optionally with a barrier Voigt, and signal processing for identifying cardiogenic oscillations for evaluating disease states, use in drug discovery or monitoring disease status.

Dynamic 129Xe gas exchange spectroscopy

Growing community of inventors

Durham, NC, United States of America

Ziyi Wang