Leland Stanford Junior University

Ge Precision Healthcare LLC

Christopher Michael Sandino

Shreyas S Vasanawala

Joseph Yitan Cheng

John Mark Pauly

Peng Lai

Frank Ong

Feiyu Chen

Jiacheng Jason He

Zheng Zhong

A method for magnetic resonance imaging (MRI) performs a spoiled gradient-recalled (SPGR) MRI scan with an MRI scanner to produce MRI data; and reconstructs an MRI image from the MRI data; wherein performing the SPGR MRI scan comprises playing an interleaved-randomized spoiler (IRS) gradient after every M-th acquisition block, where M≥2, and where an absolute area of the IRS gradient of each IRS is randomized between zero and a maximum gradient area achievable on the MRI scanner.

Magnetic resonance imaging using 3D spoiled gradient-recalled sequence

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 is disclosed for phase contrast magnetic resonance imaging (MRI) comprising: acquiring phase contrast 3D spatiotemporal MRI image data; inputing the 3D spatiotemporal MRI image data to a three-dimensional spatiotemporal convolutional neural network to produce a phase unwrapping estimate; generating from the phase unwrapping estimate an integer number of wraps per pixel; and combining the integer number of wraps per pixel with the phase contrast 3D spatiotemporal MRI image data to produce final output.

Method for correction of phase-contrast magnetic resonance imaging data using a neural network

A method for magnetic resonance imaging (MRI) includes steps of acquiring by an MRI scanner undersampled magnetic-field-gradient-encoded k-space data; performing a self-calibration of a magnetic-field-gradient-encoding point-spread function using a first neural network to estimate systematic waveform errors from the k-space data, and computing the magnetic-field-gradient-encoding point-spread function from the systematic waveform errors; reconstructing an image using a second neural network from the magnetic-field-gradient-encoding point-spread function and the k-space data.

Method for estimating systematic imperfections in medical imaging systems with deep learning

Various methods and systems are provided for reconstructing magnetic resonance images from accelerated magnetic resonance imaging (MRI) data. In one embodiment, a method for reconstructing a magnetic resonance (MR) image includes: estimating multiple sets of coil sensitivity maps from undersampled k-space data, the undersampled k-space data acquired by a multi-coil radio frequency (RF) receiver array; reconstructing multiple initial images using the undersampled k-space data and the estimated multiple sets of coil sensitivity maps; iteratively reconstructing, with a trained deep neural network, multiple images by using the initial images and the multiple sets of coil sensitivity maps to generate multiple final images, each of the multiple images corresponding to a different set of the multiple sets of sensitivity maps; and combining the multiple final images output from the trained deep neural network to generate the MR image.

Growing community of inventors

Menlo Park, CA, United States of America

Christopher Michael Sandino