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    Free-breathing cine DENSE imaging
    1.
    发明授权
    Free-breathing cine DENSE imaging 有权

    公开(公告)号：US10830855B2

    公开(公告)日：2020-11-10

    申请号：US16368218

    申请日：2019-03-28

    Applicant: University of Virginia Patent Foundation

    Inventor： Xiaoying Cai ,  Frederick H. Epstein

    IPC: G01R33/565 ,  G01R33/56 ,  G06T5/50 ,  G06T11/00 ,  G06T7/00 ,  G06T5/00 ,  G06T7/20 ,  A61B5/055 ,  A61B5/00 ,  G01R33/567

    Abstract: In some aspects, the disclosed technology relates to free-breathing cine DENSE (displacement encoding with stimulated echoes) imaging. In some embodiments, self-gated free-breathing adaptive acquisition reduces free-breathing artifacts by minimizing the residual energy of the phase-cycled T1-relaxation signal, and the acquisition of the k-space data is adaptively repeated with the highest residual T1-echo energy. In some embodiments, phase-cycled spiral interleaves are identified at matched respiratory phases by minimizing the residual signal due to T1 relaxation after phase-cycling subtraction; image-based navigators (iNAVs) are reconstructed from matched phase-cycled interleaves that are comprised of the stimulated echo iNAVs (ste-iNAVs), wherein the ste-iNAVs are used for motion estimation and compensation of k-space data.

    Multiband spiral cardiac MRI with non-cartesian reconstruction methods
    4.
    发明授权
    Multiband spiral cardiac MRI with non-cartesian reconstruction methods 有权

    公开(公告)号：US11320506B2

    公开(公告)日：2022-05-03

    申请号：US16843869

    申请日：2020-04-08

    Applicant: University of Virginia Patent Foundation

    Inventor： Changyu Sun ,  Frederick H. Epstein ,  Yang Yang ,  Xiaoying Cai ,  Michael Salerno ,  Craig H. Meyer ,  Daniel Stuart Weller

    IPC: G01R33/48 ,  G01R33/36 ,  G01R33/567 ,  G06T7/262 ,  G01R33/58

    Abstract: 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.

    Systems and Methods for Three-Dimensional Spiral Perfusion Imaging
    7.
    发明申请
    Systems and Methods for Three-Dimensional Spiral Perfusion Imaging 有权
    三维螺旋灌注成像系统与方法

    公开(公告)号：US20160148378A1

    公开(公告)日：2016-05-26

    申请号：US14952859

    申请日：2015-11-25

    Applicant: UNIVERSITY OF VIRGINIA PATENT FOUNDATION

    Inventor： Michael Salerno ,  Craig H. Meyer ,  Xiao Chen ,  Yang Yang ,  Frederick H. Epstein ,  Christopher M. Kramer

    IPC: G06T7/00 ,  G06T15/08 ,  G06T11/00 ,  G06T7/20

    CPC classification number: G06T11/003 ,  A61B5/0044 ,  A61B5/0263 ,  A61B5/055 ,  G01R33/4826 ,  G01R33/5601 ,  G01R33/5611 ,  G01R33/56366

    Abstract: Some aspects of the present disclosure relate to systems and methods for three-dimensional spiral perfusion imaging. In one embodiment, a method for perfusion imaging of a subject includes acquiring perfusion imaging data associated with the heart of a subject. The acquiring includes applying an imaging pulse sequence with a three-dimensional stack-of-spirals trajectory. The method also includes reconstructing perfusion images from the acquired perfusion imaging data. The reconstructing includes parallel imaging and motion-guided compressed sensing. The method also includes determining, from the reconstructed perfusion images, absolute perfusion values based on time-intensity relationships to quantify myocardial blood flow of the heart of the subject, and generating a quantitative volumetric perfusion flow map based on the determined absolute perfusion values.

    Abstract translation: 本公开的一些方面涉及用于三维螺旋灌注成像的系统和方法。 在一个实施例中，用于对象的灌注成像的方法包括获取与受试者的心脏相关联的灌注成像数据。 该采集包括应用具有三维叠加螺旋轨迹的成像脉冲序列。 该方法还包括从获取的灌注成像数据重建灌注图像。 重建包括并行成像和运动引导压缩感测。 该方法还包括根据重建的灌注图像确定基于时间 - 强度关系的绝对灌注值，以量化受试者心脏的心肌血流量，以及基于确定的绝对灌注值产生定量体积灌注流程图。

    Methods and Systems for Intramyocardial Tissue Displacement and Motion Measurement
    9.
    发明公开
    Methods and Systems for Intramyocardial Tissue Displacement and Motion Measurement 审中-公开

    公开(公告)号：US20240197262A1

    公开(公告)日：2024-06-20

    申请号：US18472215

    申请日：2023-09-21

    Applicant: University of Virginia Patent Foundation

    Inventor： Frederick H. Epstein ,  Yu Wang ,  Changyu Sun

    IPC: A61B5/00 ,  G06V10/44

    CPC classification number: A61B5/7267 ,  G06V10/44

    Abstract: An exemplary method and system are disclosed that employ deep learning neural-network(s) trained with displacement-encoded imaging data (i.e., DENSE data) to estimate intramyocardial motion from cine MRI images retrieved with balanced steady state free precession sequences (bFSSP) and other cardiac medical imaging modalities, including standard cardiac computer tomography (CT) images, magnetic resonance imaging (MRI) images, echocardiogram images, heart ultrasound images, among other medical imaging modalities described herein. The deep learning neural-network(s) can be trained using (i) contour motion data from displacement-encoded imaging magnitude data as inputs to the neural network and (ii) displacement maps derived from displacement-encoded imaging phase images for comparison to the outputs of the neural network for neural network adjustments during the training. The DENSE trained neural network can be used to calculate tissue displacement from bFSSP cine images.

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