公开(公告)号：US10722137B2

公开(公告)日：2020-07-28

申请号：US14677915

申请日：2015-04-02

Applicant: University of Virginia Patent Foundation ,  The Board of Trustees of the Leland Stanford Junior University

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

IPC: A61B5/055 ,  A61N7/02 ,  A61B5/01 ,  A61B5/00 ,  A61B18/00 ,  A61B90/00

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.

公开(公告)号：US11950877B2

公开(公告)日：2024-04-09

申请号：US17428832

申请日：2020-02-05

Applicant: UNIVERSITY OF VIRGINIA PATENT FOUNDATION

Inventor： Craig H. Meyer ,  Xue Feng ,  Michael Salerno

IPC: A61B5/00 ,  A61B5/026 ,  A61B5/055 ,  G01R33/483 ,  G01R33/563 ,  G06N3/08 ,  G06T3/00 ,  G06T3/60 ,  G06T7/00 ,  G06T7/11 ,  G06T7/143 ,  G06T7/149 ,  G06T7/194 ,  G06T7/215 ,  G06T7/33 ,  G16H30/40

CPC classification number: A61B5/0044 ,  A61B5/0263 ,  A61B5/055 ,  A61B5/7267 ,  G01R33/4835 ,  G01R33/56366 ,  G06N3/08 ,  G06T3/0006 ,  G06T3/60 ,  G06T7/0014 ,  G06T7/11 ,  G06T7/143 ,  G06T7/149 ,  G06T7/194 ,  G06T7/215 ,  G06T7/33 ,  G16H30/40 ,  A61B2576/023 ,  G06T2207/10088 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30048 ,  G06T2207/30104 ,  G06T2207/30168

Abstract: 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.

公开(公告)号：US11857288B2

公开(公告)日：2024-01-02

申请号：US17166604

申请日：2021-02-03

Applicant: University of Virginia Patent Foundation

Inventor： Sona Ghadimi ,  Changyu Sun ,  Xue Feng ,  Craig H. Meyer ,  Frederick H. Epstein

IPC: G06T7/00 ,  A61B5/00 ,  A61B5/02 ,  G06T7/11 ,  G16H30/40 ,  G06N3/08 ,  G01R33/56 ,  A61B5/055

CPC classification number: A61B5/0044 ,  A61B5/02028 ,  A61B5/7267 ,  A61B5/7278 ,  G01R33/56 ,  G06N3/08 ,  G06T7/0012 ,  G06T7/11 ,  G16H30/40 ,  A61B5/055 ,  A61B2576/023 ,  G06T2207/10088 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30048

Abstract: 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.

公开(公告)号：US11813047B2

公开(公告)日：2023-11-14

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 ,  G06F18/211 ,  G06V10/764 ,  G06V10/82

CPC classification number: A61B5/0263 ,  A61B5/029 ,  A61B5/1071 ,  A61B5/1075 ,  A61B5/7267 ,  G06F18/211 ,  G06T7/0012 ,  G06T7/11 ,  G06T7/13 ,  G06T7/62 ,  G06T7/70 ,  G06V10/764 ,  G06V10/82 ,  G06T2207/10088 ,  G06T2207/20084 ,  G06T2207/30048 ,  G06V2201/031

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：US11747419B2

公开(公告)日：2023-09-05

申请号：US17733970

申请日：2022-04-29

Applicant: University of Virginia Patent Foundation

Inventor： Zhixing Wang ,  Xue Feng ,  John P. Mugler, III ,  Michael Salerno ,  Adrienne E. Campbell-Washburn ,  Craig H. Meyer

IPC: G01R33/48 ,  G01R33/561 ,  G01R33/44 ,  G01R33/565 ,  G01R33/54

CPC classification number: G01R33/4818 ,  G01R33/448 ,  G01R33/543 ,  G01R33/5613 ,  G01R33/56518

Abstract: 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.