Leland Stanford Junior University

General Electric Company

Brian A Hargreaves

Bruce L Daniel

Shreyas S Vasanawala

Yuxin Hu

Valentina Taviani

John Mark Pauly

Steven M Conolly

Garry Evan Gold

Xinwei Shi

Evan G Levine

Wenmiao Lu

Hans Weber

Bragi Sveinsson

Kevin Franklin King

Scott Brian Reeder

Graeme Colin McKinnon

Dwight G Nishimura

Fraser John Laing Robb

Kevin Matthew Koch

Jean Helen Brittain

Tao Zhang

Joseph Yitan Cheng

Krishna S Nayak

Kim Butts Pauly

Charles H Cunningham

Paul David Calderon

Suchandrima Banerjee

Thomas Grafendorfer

Feiyu Chen

Neal K Bangerter

Brady J Quist

Subashini Srinivasan

Daehyun Yoon

Seungbum Koo

Anderson Nnewihe

Kristin L Granlund

Ernesto Staroswiecki

Pauline W Worters

A diffusion-weighted magnetic resonance imaging (MRI) method acquires MRI scan data from a multi-direction, multi-shot, diffusion-weighted MRI scan, and jointly reconstructs from the MRI scan data 1) magnitude images for multiple diffusion-encoding directions and 2) phase images for multiple shots and multiple diffusion-encoding directions using an iterative reconstruction method. Each iteration of the iterative reconstruction method comprises a gradient calculation, a phase update to update the phase images, and a magnitude update to update the magnitude images. Each iteration minimizes a cost function comprising a locally low-rank (LLR) regularization constraint on the magnitude images from the multiple diffusion-encoding directions.

Diffusion-weighted MRI with magnitude-based locally low-rank regularization

Multi-shot diffusion-weighted magnetic resonance imaging acquires multiple k-space segments of diffusion-weighted MRI data, estimates reconstructed multi-shot diffusion weighted images, and combines the estimated images to obtain a final reconstructed MRI image. The estimation of images iteratively calculates updated multi-shot images from the multiple k-space segments and current multi-shot images using a convex model without estimating motion-induced phase, constructs multiple locally low-rank spatial-shot matrices from the updated multi-shot images, and calculates current multi-shot images from spatial-shot matrices.

Motion robust reconstruction of multi-shot diffusion-weighted images without phase estimation via locally low-rank regularization

A method for magnetic resonance imaging reconstructs images that have reduced under-sampling artifacts from highly accelerated multi-spectral imaging acquisitions. The method includes performing by a magnetic resonance imaging (MRI) apparatus an accelerated multi-spectral imaging (MSI) acquisition within a field of view of the MRI apparatus, where the sampling trajectories of different spectral bins in the acquisition are different; and reconstructing bin images using neural network priors learned from training data as regularization to reduce under-sampling artifacts.

Deep learning based methods to accelerate multi-spectral imaging

A method of magnetic resonance imaging performs a scan by a magnetic resonance imaging system to acquire k-space data; applies the k-space data as input to an unrolled convolutional neural network comprising multiple iterations, and generates reconstructed images from the output of the unrolled convolutional neural network by combining images from different shots. Each iteration of the unrolled network performs a first gradient update, applies the result to a first U-net in k-space, performs a second gradient update, and applies a second U-net in image space. The first gradient update and the second gradient update are based on a theoretical gradient from a physical measurement model.

Multi-shot diffusion-weighted MRI reconstruction using unrolled network with U-net as priors

A method for providing an estimated 3D Tmap for magnetic resonance imaging using a Double-Echo Steady-State (DESS) sequence for a volume of an object in a magnetic resonance imaging (MRI) system is provided. A DESS scan of the volume is provided by the MRI system. Signals Sand Sare acquired by the MRI system. Signals Sand Sare used to provide a Tmap for a plurality of slices of the volume, comprising determining repetition time (TR), echo time (TE), flip angle &#x3b1;, and an estimate of the longitudinal relaxation time (T), and wherein the DESS scan has a spoiler gradient with an amplitude G and a duration &#x3c4; and ignoring echo pathways having spent more than two repetition times in the transverse plane.

Method for estimating T2

A method for magnetic resonance imaging suppresses off-resonance gradient-induced image artifacts due to metal. The method includes performing by a magnetic resonance imaging (MRI) apparatus two multi-spectral imaging (MSI) acquisitions within a field of view of the MRI apparatus, where the two MSI acquisitions have alternating-sign readout gradients. The two MSI acquisitions are then processed and combined by the MRI apparatus using a weighted image combination to produce a final image.

Alternating gradients for metal-induced artifacts correction in magnetic resonance imaging

A method for generating a magnetic resonance image of an object in a magnetic resonance imaging (MRI) system, wherein the object contains at least one metallic implant is provided. The MRI system provides multiple excitations of at least part of the object. The MRI system reads out image signals from the object. The MRI system saves the readout image signals as image data. A field-map is generated from the image data using a goodness-of-fit process which uses a goodness-of-fit metric, matched-filter, and/or similar fitting techniques to fit expected signals from each excitation to the image data.

Field-mapping and artifact correction in multispectral imaging

Improved motion correction for magnetic resonance imaging is provided. An MR imaging method provides a first sequence of MR images and a second sequence of MR images where: 1) the two sequences are inherently spatially co-registered and synchronous with each other; 2) the first sequence includes signal variation due to one or more causes other than motion or deformation; and 3) the second sequence does not include the signal variation of the first sequence. In this situation, the second sequence can be used to perform motion correction for the first sequence. One example of this approach is Dixon MR imaging, where the water images are the first sequence and the fat images are the second sequence.

Motion correction in two-component magnetic resonance imaging

A method for performing wave-encoded magnetic resonance imaging of an object is provided. The method includes applying one or more wave-encoded magnetic gradients to the object, and acquiring MR signals from the object. The method further includes calibrating a wave point-spread function, and reconstructing an image from the MR signals based at least in part on the calibrated wave point-spread function. Calibration of the wave point-spread function is based at least in part on one or more intermediate images generated from the MR signals.

System and method for performing wave-encoded magnetic resonance imaging of an object

A method for providing at least one measurement by a magnetic resonance imaging (MRI) system of a tissue property or underlying tissue property in a region sufficiently close to a metal object, so that the metal object induces artifacts is provided. At least one magnetic resonance imaging signal from the region is acquired through the MRI system. The acquired at least one MRI signal is processed to correct for artifacts induced by the metal object. At least one tissue property or underlying tissue property measurement is extracted from the processed at least one MRI signal.

Quantitative MRI measurements near metal objects

A method using 2D multi-spectral imaging (2DMSI) for MRI imaging of a metallic object (such as a biopsy needle) and region surrounding the metallic object within an imaging field of view of an MRI apparatus includes segmenting the imaging field-of-view into spatial-spectral bins, where the segmenting is based on off-resonance frequency induced by the metallic object and slice location; selectively exciting each frequency bin of the spatial-spectral bins by inverting a slice selection gradient between excitation and refocusing pulses; performing repeated acquisition with different radiofrequency modulations to produce acquired images of adjacent bins; composing a 2DMSI image by root-sum-of-squares combination of the acquired images of adjacent bins; and highlighting in the 2DMSI image an area of furthest off-resonance bins based on 2DMSI off-resonance information by thresholding image intensity in frequency bins, thereby indicating a contour of the metallic object.

Passive MRI-guided tracking of metallic objects using artifact-reduced imaging with selective excitation

A method for magnetic resonance imaging is provided that includes using a magnetic resonance imaging system to excite a field of view (FOV) for a target being imaged, using an excitation plan to limit the excited FOV to a relatively narrow band of magnetization, exciting multiple bands of magnetization simultaneously, applying phase encoding along a shortest FOV dimension, acquiring a signal from said simultaneously excited bands of magnetization, and reconstructing and outputting a target image from the acquired signal.

High resolution magnetic resonance imaging with reduced distortion based on reduced-field-of-view and generalized parallel imaging

Accelerated 3D multispectral imaging (MSI) on a magnetic resonance imaging (MRI) system uses phase-encoding in two dimensions and frequency-encoding in the third. The method generates randomized k-space undersampling patterns that vary between different spectral bins to determine k-space samples to be acquired in each spectral bin; orders k-space samples into echo trains that determine gradient waveforms; initializes the gradient waveforms, RF waveforms, and timing information; plays the gradient and RF waveforms using the timing information to excite and refocus different spatial and spectral bin regions; acquires undersampled MRI signal data on the MRI system from the spatial and spectral bin regions; uses robust principal component analysis to reconstruct on-resonance images and off-resonance images represented as sets of low rank and sparse matrices from the undersampled MRI signal data; combines the on-resonance images and off-resonance images to yield a final image; and presents the final image on a display.

Robust principal component analysis for separation of on and off-resonance in 3D multispectral MRI

A method for 3D magnetic resonance imaging (MRI) with slice-direction distortion correction is provided. One or more selective cross-sections with a thickness along a first axis are excited using a RF pulse with a bandwidth, wherein a selective cross-section is either a selective slice or selective slab. A refocusing pulse is applied to form a spin echo. One or more 2D encoded image signals are acquired with readout along a second axis and phase encoding along a third axis, wherein the data long the phase encoded first and third axes is acquired with an under sampling scheme. Slice-direction distortion is corrected by resolving the position by using phase encoding.

Distortion-free magnetic resonance imaging near metallic implants

A method of ordering slices for interleaved MRI is provided that includes selecting a number of interleaved slice locations (N) each having a plurality of excitations (N), where S is the slice number between 1 and Nand Nmay differ for different slice numbers, selecting an excitation duration (T) of each the excitation in each the slice, selecting a repetition time (TR) between successive excitations of the same slice, TR has a duration of N&#xd7;T, Nis the number of interleaved slices per TR period, and arranging the order for the slices such that the total scan time (T) is minimized such that TR is the product of Nand T, where Ncan be arbitrarily chosen between 1 and N/Nwhere Nis the total number of excitations for all the slices and where Nis the maximum number of the excitations for one slice.

Growing community of inventors

Menlo Park, CA, United States of America

Brian A Hargreaves