公开(公告)号：US11921179B2

公开(公告)日：2024-03-05

申请号：US17732181

申请日：2022-04-28

Applicant: University of Virginia Patent Foundation

Inventor： Zhixing Wang ,  Steven P. Allen ,  Xue Feng ,  John P. Mugler, III ,  Craig H. Meyer

IPC: G01R33/48 ,  G01R33/54 ,  G01R33/56 ,  G01R33/561 ,  G01R33/565

CPC classification number: G01R33/482 ,  G01R33/543 ,  G01R33/5608 ,  G01R33/5617 ,  G01R33/56509

Abstract: 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.

公开(公告)号：US20220373630A1

公开(公告)日：2022-11-24

申请号：US17733967

申请日：2022-04-29

Applicant: University of Virginia Patent Foundation

Inventor： Quan Dou ,  Zhixing Wang ,  Xue Feng ,  John P. Mugler, III ,  Craig H. Meyer

IPC: G01R33/56 ,  G01R33/561

Abstract: 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.

公开(公告)号：US20210287367A1

公开(公告)日：2021-09-16

申请号：US17335518

申请日：2021-06-01

Applicant: University of Virginia Patent Foundation

Inventor： Craig H. Meyer ,  Anudeep Konda ,  Christopher M. Kramer ,  Xue Feng

IPC: G06T7/00 ,  G06T7/62 ,  G06T7/13 ,  G06T7/70 ,  G06K9/62 ,  A61B5/029 ,  A61B5/026 ,  A61B5/00 ,  A61B5/107 ,  G06T7/11

Abstract: 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.

公开(公告)号：US11024025B2

公开(公告)日：2021-06-01

申请号：US16295939

申请日：2019-03-07

Applicant: University of Virginia Patent Foundation

Inventor： Craig H. Meyer ,  Anudeep Konda ,  Christopher M. Kramer ,  Xue Feng

IPC: G06T7/00 ,  G06T7/62 ,  G06T7/13 ,  G06T7/70 ,  G06K9/62 ,  A61B5/029 ,  A61B5/026 ,  A61B5/00 ,  A61B5/107 ,  G06T7/11

Abstract: 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.

公开(公告)号：US11860258B2

公开(公告)日：2024-01-02

申请号：US17732155

申请日：2022-04-28

Applicant: University of Virginia Patent Foundation ,  THE UNITED STATES OF AMERICA, AS REPRESENTED BY THE SECRETARY DEPARTMENT OF HEALTH AND HUMAN SERVICES ,  Siemens Healthcare GMBH

Inventor： John P. Mugler, III ,  Craig H. Meyer ,  Adrienne Campbell ,  Rajiv Ramasawmy ,  Josef Pfeuffer ,  Zhixing Wang ,  Xue Feng

IPC: G01R33/565 ,  G01R33/561

CPC classification number: G01R33/56581 ,  G01R33/5618

Abstract: 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.

公开(公告)号：US20230380714A1

公开(公告)日：2023-11-30

申请号：US18305296

申请日：2023-04-21

Applicant: University of Virginia Patent Foundation

Inventor： Quan Dou ,  Zhixing Wang ,  Xue Feng ,  Craig H. Meyer

IPC: A61B5/055

CPC classification number: A61B5/055 ,  G06T2207/20081 ,  G06T2207/10088 ,  G06T2207/20084

Abstract: Blurring and noise artifacts in magnetic resonance (MR) images caused by off-resonant image components may be corrected with convolutional neural networks, particularly feed forward networks with skip connections. Demodulating complex blurred images with off-resonant artifacts at a selected number of frequencies forms a respective real component frame of the MR data and a respective imaginary component frame for each image. A convolutional neural network is used to de-blur the images. The network has a plurality of residual blocks with multiple convolution calculations paired with respective skip connections. The method outputs, from the convolutional neural network, a de-blurred real image frame and a de-blurred imaginary image frame of the MR data for each complex blurred image.

公开(公告)号：US20190279361A1

公开(公告)日：2019-09-12

申请号：US16295939

申请日：2019-03-07

Applicant: University of Virginia Patent Foundation

Inventor： Craig H. Meyer ,  Anudeep Konda ,  Christopher M. Kramer ,  Xue Feng

IPC: G06T7/00 ,  G06T7/11 ,  G06T7/62 ,  G06T7/13 ,  G06T7/70 ,  G06K9/62 ,  A61B5/029 ,  A61B5/026 ,  A61B5/00 ,  A61B5/107

Abstract: 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.

公开(公告)号：US09910118B2

公开(公告)日：2018-03-06

申请号：US13867922

申请日：2013-04-22

Applicant: University of Virginia Patent Foundation

Inventor： Xue Feng ,  Michael Salerno ,  Christopher M. Kramer ,  Craig H. Meyer

IPC: G01V3/00 ,  G01R33/48 ,  G01R33/56 ,  G01R33/563 ,  G01R33/561

CPC classification number: G01R33/482 ,  G01R33/5608 ,  G01R33/5611 ,  G01R33/56308

Abstract: Systems and methods for Cartesian dynamic imaging are disclosed. In one aspect, in accordance with one example embodiment, a method includes acquiring magnetic resonance data for an area of interest of a subject that is associated with one or more physiological activities of the subject and performing image reconstruction comprising Kalman filtering or smoothing on Cartesian images associated with the acquired magnetic resonance data. Performing the image reconstruction includes increasing at least one of spatial and temporal resolution of the Cartesian images.

公开(公告)号：US20150282733A1

公开(公告)日：2015-10-08

申请号：US14677915

申请日：2015-04-02

Applicant: University of Virginia Patent Foundation

Inventor： Samuel Fielden ,  Li Zhao ,  Wilson Miller ,  Xue Feng ,  Max Wintermark ,  Kim Butts Pauly ,  Craig H. Meyer

IPC: A61B5/055 ,  A61N7/02 ,  A61B5/01

CPC classification number: A61B5/055 ,  A61B5/015 ,  A61B5/725 ,  A61B2018/00791 ,  A61B2090/374 ,  A61N7/02

Abstract: Aspects of the present disclosure relate to magnetic resonance thermometry. In one embodiment, a method includes acquiring undersampled magnetic resonance data associated with an area of interest of a subject receiving focused ultrasound treatment, and reconstructing images corresponding to the area of interest based on the acquired magnetic resonance data, where the reconstructing uses Kalman filtering.

Abstract translation: 本公开的方面涉及磁共振测温。 在一个实施例中，一种方法包括获取与接收聚焦超声治疗的受试者的感兴趣区域相关联的欠采样磁共振数据，以及基于所获取的磁共振数据重构与所述感兴趣区域相对应的图像，其中所述重建使用卡尔曼滤波。

公开(公告)号：US11954578B2

公开(公告)日：2024-04-09

申请号：US17605078

申请日：2020-04-24

Applicant: UNIVERSITY OF VIRGINIA PATENT FOUNDATION

Inventor： Craig H Meyer ,  Xue Feng

IPC: G06N3/045 ,  A61B5/055 ,  G01R33/56 ,  G06N3/047 ,  G06N3/088 ,  G06T5/00

CPC classification number: G06N3/045 ,  A61B5/055 ,  G01R33/5608 ,  G06N3/047 ,  G06N3/088 ,  G06T5/002 ,  G06T2200/04 ,  G06T2207/10092 ,  G06T2207/30016

Abstract: 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.