SYSTEMS AND METHODS FOR PHASE UNWRAPPING FOR DENSE MRI USING DEEP LEARNING

    公开(公告)号:US20210267455A1

    公开(公告)日:2021-09-02

    申请号:US17166604

    申请日:2021-02-03

    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.

    FREE-BREATHING CINE DENSE IMAGING
    15.
    发明申请

    公开(公告)号:US20190302211A1

    公开(公告)日:2019-10-03

    申请号:US16368218

    申请日:2019-03-28

    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.

    Systems and methods for free-breathing cine DENSE MRI using self-navigation

    公开(公告)号:US10310047B2

    公开(公告)日:2019-06-04

    申请号:US15493825

    申请日:2017-04-21

    Abstract: Some aspects of the present disclosure relate to systems and methods for free-breathing cine DENSE MRI using self-navigation. In one embodiment, a method includes acquiring magnetic resonance data for an area of interest of a subject, wherein the acquiring comprises performing sampling with phase-cycled, cine displacement encoding with stimulated echoes (DENSE) during free-breathing of the subject; identifying, from the acquired magnetic resonance data, a plurality of phase-cycling data pairs corresponding to matched respiratory phases of the free-breathing of the subject; reconstructing, from the plurality of phase-cycling data pairs, a plurality of intermediate self-navigation images; performing motion correction by estimating, from the plurality of intermediate self-navigation images, the respiratory position associated with the plurality of phase-cycling data pairs; and reconstructing a plurality of motion-corrected cine DENSE images of the area of interest of the subject.

    SYSTEMS AND METHODS FOR FREE-BREATHING CINE DENSE MRI USING SELF-NAVIGATION

    公开(公告)号:US20170307712A1

    公开(公告)日:2017-10-26

    申请号:US15493825

    申请日:2017-04-21

    CPC classification number: G01R33/5676 G01R33/561 G01R33/56316 G01R33/56325

    Abstract: Some aspects of the present disclosure relate to systems and methods for free-breathing cine DENSE MRI using self-navigation. In one embodiment, a method includes acquiring magnetic resonance data for an area of interest of a subject, wherein the acquiring comprises performing sampling with phase-cycled, cine displacement encoding with stimulated echoes (DENSE) during free-breathing of the subject; identifying, from the acquired magnetic resonance data, a plurality of phase-cycling data pairs corresponding to matched respiratory phases of the free-breathing of the subject; reconstructing, from the plurality of phase-cycling data pairs, a plurality of intermediate self-navigation images; performing motion correction by estimating, from the plurality of intermediate self-navigation images, the respiratory position associated with the plurality of phase-cycling data pairs; and reconstructing a plurality of motion-corrected cine DENSE images of the area of interest of the subject.

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