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    Multiband spiral cardiac MRI with non-cartesian reconstruction methods
    2.
    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.

    Methods and Systems for Intramyocardial Tissue Displacement and Motion Measurement
    3.
    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.

    SYSTEMS AND METHODS FOR PHASE UNWRAPPING FOR DENSE MRI USING DEEP LEARNING
    6.
    发明申请
    SYSTEMS AND METHODS FOR PHASE UNWRAPPING FOR DENSE MRI USING DEEP LEARNING 有权

    公开(公告)号：US20210267455A1

    公开(公告)日：2021-09-02

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

    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.

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