University of Virginia Patent Foundation

Leland Stanford Junior University

The United States of America, As Represented by the Secretary, Department of Health and Human Services

Siemens Healthcare Gmbh

Other

Springbok, Inc.

Craig H Meyer

Xue Feng

John P Mugler, Iii

Samuel W Fielden

Frederick H Epstein

Michael Salerno

Li Zhao

John Mark Pauly

Weitian Chen

Christopher M Kramer

Zhixing Wang

Max Wintermark

Xiao Chen

Josef Pfeuffer

Yang Yang

Peng Hu

Anudeep Konda

Albert Macovski

Berthold Kiefer

Dwight G Nishimura

Kim Butts Pauly

Adrienne Campbell

Xiaoying Cai

G Wilson Miller, Iv

Silvia Blemker

Daniel Stuart Weller

Changyu Sun

Manal Nicolas-Jilwan

Steven P Allen

Rajiv Ramasawmy

Norbert Joseph Pelc

Alto Stemmer

Alexander L Klibanov

Gudrun Ruyters

Daniel Mark Spielman

Joseph M Hart

Amit Patel

Pablo Irarrazabal

George A Beller

David K Glover

Kenneth O Johnson

Dayuan Li

Christopher T Sica

Hao Tan

Renkun Ni

Mohammad Abdishektaei

Helen L Sporkin

Yekaterina K Gilbo

Wilson Miller

Todd S Sachs

Talissa A Altes

Gerard T Luk Pat

Adrienne E Campbell-Washburn

Quan Dou

Katherine R Knaus

Sona Ghadimi

Geoffrey Handsfield

Grady W Miller, Iv

Ahsan Javed

Luonan Wang

Mark F Abel

Silvia S Blemker

Geoffrey G Handsfield

Acquiring 3D MRI data using spiral-in-out encoding trajectories includes calculating a variable flip angle RF series for use as refocusing pulses, wherein the RF series includes a plurality of refocusing RF pulses. A spoiler gradient waveform is applied along the spoiler gradient direction, wherein the computer alternately adds and subtracts partition encoding waveforms to the spoiler gradient waveform. The method reads MRI data from each encoding step during an MRI sequence. The MRI sequence inserts a spiral-in gradient before a first refocusing RF pulse from the RF sequence, overlaps a pre-winder lobe for the encoding trajectory with the spoiler gradient waveform having the partition encoding waveforms added therein, and overlaps a rewinder lobe for the encoding trajectory with the spoiler gradient waveform having the partition encoding waveforms subtracted there from.

System and method for variable-flip-angle 3D spiral-in-out TSE/space using echo-reordering and concomitant gradient compensation

Systems and methods for denoising a magnetic resonance (MR) image utilize an unsupervised deep convolutional neural network (U-DCNN). Magnetic resonance (MR) image data of an area of interest of a subject can be acquired, which can include noisy input images that comprise noise data and noise free image data. For each of the noisy input images, iterations can be run of a converging sequence in an unsupervised deep convolutional neural network. In each iteration, parameter settings are updated; the parameter settings are used in calculating a series of image feature sets with the U-DCNN. The image feature sets predict an output image. The converging sequence of the U-DCNN is terminated before the feature sets predict a respective output image that replicates all of the noise data from the noisy input image. Based on a selected feature set, a denoised MR image of the area of interest of the subject can be output.

Denoising magnetic resonance images using unsupervised deep convolutional neural networks

A computerized system and method of modeling myocardial tissue perfusion can include acquiring a plurality of original frames of magnetic resonance imaging (MRI) data representing images of a heart of a subject and developing a manually segmented set of ground truth frames from the original frames. Applying training augmentation techniques to a training set of the originals frame of MRI data can prepare the data for training at least one convolutional neural network (CNN). The CNN can segment the training set of frames according to the ground truth frames. Applying the respective input test frames to a trained CNN can allow for segmenting an endocardium layer and an epicardium layer within the respective images of the input test frames. The segmented images can be used in calculating myocardial blood flow into the myocardium from segmented images of the input test frames.

System and method for fully automatic LV segmentation of myocardial first-pass perfusion images

Methods, computing devices, and magnetic resonance imaging systems that improve image quality in turbo spiral echo (TSE) imaging are disclosed. With this technology, a TSE pulse sequence is generated that includes a series of radio frequency (RF) refocusing pulses to produce a corresponding series of nuclear magnetic resonance (NMR) spin echo signals. A gradient waveform including a plurality of segments is generated. The plurality of segments collectively comprise a spiral ring retraced in-out trajectory. During an interval adjacent to each of the series of RF refocusing pulses, a first gradient pulse is generated according to the gradient waveform. The first gradient pulses encode the NMR spin echo signals. An image is then constructed from digitized samples of the NMR spin echo signals obtained based at least in part on the encoding.

Methods and systems for spin-echo train imaging using spiral rings with retraced trajectories

Methods, computing devices, and MRI systems that reduce artifacts produced by Maxwell gradient terms in TSE imaging using non-rectilinear trajectories are disclosed. With this technology, a RF excitation pulse is generated to produce transverse magnetization that generates a NMR signal and a series of RF refocusing pulses to produce a corresponding series of NMR spin-echo signals. An original encoding gradient waveform comprising a non-rectilinear trajectory is modified by adjusting a portion of the original encoding gradient waveform or introducing a zero zeroth-moment waveform segment at end(s) of the original encoding gradient waveform. During an interval adjacent to each of the series of RF refocusing pulses a first gradient pulse is generated. At least one of the first gradient pulses is generated according to the modified gradient waveform. An image is constructed from generated digitized samples of the NMR spin-echo signals obtained.

Methods and systems for Maxwell compensation for spin-echo train imaging

A method of cardiac strain analysis uses displacement encoded magnetic resonance image (MRI) data of a heart of the subject and includes generating a phase image for each frame of the displacement encoded MRI data. Phase images include potentially phase-wrapped measured phase values corresponding to pixels of the frame. A convolutional neural network CNN computes a wrapping label map for the phase image, and the wrapping label map includes a respective number of phase wrap cycles present at each pixel in the phase image. Computing an unwrapped phase image includes adding a respective phase correction to each of the potentially-wrapped measured phase values of the phase image, and the phase correction is based on the number of phase wrap cycles present at each pixel. Computing myocardial strain follows by using the unwrapped phase image for strain analysis of the subject.

Systems and methods for phase unwrapping for dense MRI using deep learning

Training a neural network to correct motion-induced artifacts in magnetic resonance images includes acquiring motion-free magnetic resonance image (MRI) data of a target object and applying a spatial transformation matrix to the motion-free MRI data. Multiple frames of MRI data are produced having respective motion states. A Non-uniform Fast Fourier Transform (NUFFT) can be applied to generate respective k-space data sets corresponding to each of the multiple frames of MRI; the respective k-space data sets can be combined to produce a motion-corrupted k-space data set and an adjoint NUFFT can be applied to the motion-corrupted k-space data set. Updated frames of motion-corrupted MRI data can be formed. Using the updated frames of motion corrupted MRI data, a neural network can be trained that generates output frames of motion free MRI data; and the neural network can be saved.

Motion compensation for MRI imaging

In one aspect the disclosed technology relates to embodiments of a method which, includes acquiring magnetic resonance imaging data, for a plurality of images, of the heart of a subject. The method also includes segmenting, using cascaded convolutional neural networks (CNN), respective portions of the images corresponding to respective epicardium layers and endocardium layers for a left ventricle (LV) and a right ventricle (RV) of the heart. The segmenting is used for extracting biomarker data from segmented portions of the images and, in one embodiment, assessing hypertrophic cardiomyopathy from the biomarker data. The method further includes segmenting processes for T1 MRI data and LGE MRI data.

Automatic quantification of cardiac MRI for hypertrophic cardiomyopathy

Systems and methods for performing ungated magnetic resonance imaging are disclosed herein. A method includes producing magnetic resonance image MRI data by scanning a target in a low magnetic field with a pulse sequence having a spiral trajectory; sampling k-space data from respective scans in the low magnetic field and receiving at least one field map data acquisition and a series of MRI data acquisitions from the respective scans; forming a field map and multiple sensitivity maps in image space from the field map data acquisition; forming target k-space data with the series of MRI data acquisitions; forming initial magnetic resonance images in the image domain by applying a Non-Uniform Fast Fourier Transform to the target k-space data; and forming reconstructed images with a low rank plus sparse (L+S) reconstruction algorithm applied to the initial magnetic resonance images.

Systems and methods for spiral-in-out low field MRI scans

Aspects of the present disclosure relate to systems and methods for medical imaging that incorporate prior knowledge. Some aspects relate to incorporating prior knowledge using a non-local means filter. Some aspects relate to incorporating prior knowledge for improved perfusion imaging, such as those incorporating arterial spin labeling.

Systems and methods for medical imaging incorporating prior knowledge

Described herein are systems, methods, and computer-readable medium for magnetic resonance (MR) based thermometry. In one aspect, in accordance with one embodiment, a method for magnetic resonance based thermometry includes: acquiring, by a variable flip-angle T1 mapping sequence, MR data in an area of interest of a subject that is heated by the application of focused ultrasound (FUS) to the brain of the subject, where the MR data includes T1 values over time, and where the acquisition of the MR data includes applying an accelerated three-dimensional ultra-short spiral acquisition sequence with a nonselective excitation pulse; and determining, based at least in part on a mathematical relationship established by T1 mapping thermometry, a temperature change in the area of interest over time, and where the temperature change is caused at least in part by a change in the applied FUS.

Systems and methods for magnetic resonance based skull thermometry

A method and an apparatus for automatic muscle segmentation are provided. The method includes receiving a high-resolution three-dimensional (3D) image obtained by a magnetic resonance imaging (MM) system and splitting the high-resolution 3D image into high-resolution 3D sub-images. Furthermore, the method includes acquiring low-resolution 3D sub-images of the high-resolution 3D sub-images and determining a boundary box within each corresponding low-resolution 3D sub-image. Moreover, the method includes determining a crop box for each boundary box, cropping each high-resolution 3D sub-image based on its corresponding crop box, and segmenting at least one muscle from the cropped box high-resolution 3D sub-image.

System and method for automatic segmentation of muscles from high-resolution MRI using 3D deep convolutional neural network

A computerized method of reconstructing acquired magnetic resonance image (MRI) data to produce a series of output images includes acquiring a multiband k-space data set from a plurality of multiband slices of spiral MRI data; simultaneously acquiring a single band k-space data set comprising respective single band spiral image slices that are each associated with a respective one of the multiband slices in the multiband k-space data set; using the single band k-space data set, for each individual multiband slice, calculating a respective calibration kernel to apply to the multi-band k-space data set for each individual multiband slice; separating each individual multiband slice from the multiband k space data set by phase demodulating the multi-band k-space data using multiband phase demodulation operators corresponding to the individual multiband slice and convolving phase demodulated multi-band k-space data with a selected convolution operator to form a gridded set of the multi-band k-space data corresponding to the individual multiband slice.

Multiband spiral cardiac MRI with non-cartesian reconstruction methods

Suppressing artifacts in MRI image acquisition data includes alternatives to phase cycling by using a Convolutional Neural Network to suppress the artifact-generating echos. A U-NET CNN is trained using phase-cycled artifact-free images for ground truth comparison with received displacement encoded stimulated echo (DENSE) images. The DENSE images include data from a single acquisition with both stimulated (STE) and T1-relaxation echoes. The systems and methods of this disclosure are explained as generating artifact-free images in the ultimate output and avoiding the additional data acquisition needed for phase cycling and shortens the scan time in DENSE MRI.

Method and system for deep convolutional neural net for artifact suppression in dense MRI

Systems and methods for reconstructing a motion-compensated magnetic resonance image are presented. In certain implementations, a computer-implemented method is provided. The method may include a plurality of operations, including receiving a set of k-space data from a magnetic resonance imaging device, dividing the set of k-space data into a plurality of groups, performing a plurality of initialization operations, performing a first iterative process until a first criteria for the first iterative process is achieved for a current scale of motion estimation, performing a second iterative process until a second criteria for the second iterative process is achieved, and outputting a motion-compensated magnetic resonance image reconstructed in accordance with a predetermined scale of motion estimation.

Systems and methods for motion-compensated reconstruction of magnetic resonance images

Growing community of inventors

Charlottesville, VA, United States of America

Craig H Meyer