Abstract: An exemplary method and system are disclosed that employ DENSE deep learning neural-network(s) trained with displacement-encoded imaging data (i.e., DENSE data) to estimate intramyocardial motion from cine MRI images 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 DENSE deep learning neural-network(s) can be configured (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.

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.

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 correspond

Abstract: Suppressing artifacts in MRI image acquisition data includes alternatives to phase cycling by using a Convolutional Neural Network to suppress the artifact-generating echos. A U-NET CNN is trained using phase-cycled artifact-free images for ground truth comparison with received displacement encoded stimulated echo (DENSE) images. The DENSE images include data from a single acquisition with both stimulated (STE) and T1-relaxation echoes. The systems and methods of this disclosure are explained as generating artifact-free images in the ultimate output and avoiding the additional data acquisition needed for phase cycling and shortens the scan time in DENSE MRI.

Abstract: In one aspect the disclosed technology relates to embodiments of a method (e.g., for automatic cine DENSE strain analysis) which includes acquiring magnetic resonance data associated with a physiological activity in an area of interest of a subject where the acquired magnetic resonance data includes one or more phase-encoded data sets. The method also includes determining, from at least the one or more phase-encoded data sets, a data set corresponding to the physiological activity in the area of interest where the reconstruction comprises performing phase unwrapping of the phase-encoded data set using region growing along multiple pathways based on phase predictions.

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.

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 correspond

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.

Abstract: Suppressing artifacts in MRI image acquisition data includes alternatives to phase cycling by using a Convolutional Neural Network to suppress the artifact-generating echos. A U-NET CNN is trained using phase-cycled artifact-free images for ground truth comparison with received displacement encoded stimulated echo (DENSE) images. The DENSE images include data from a single acquisition with both stimulated (STE) and T1-relaxation echoes. The systems and methods of this disclosure are explained as generating artifact-free images in the ultimate output and avoiding the additional data acquisition needed for phase cycling and shortens the scan time in DENSE MRI.

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.

Abstract: In one aspect the disclosed technology relates to embodiments of a method (e.g., for automatic cine DENSE strain analysis) which includes acquiring magnetic resonance data associated with a physiological activity in an area of interest of a subject where the acquired magnetic resonance data includes one or more phase-encoded data sets. The method also includes determining, from at least the one or more phase-encoded data sets, a data set corresponding to the physiological activity in the area of interest where the reconstruction comprises performing phase unwrapping of the phase-encoded data set using region growing along multiple pathways based on phase predictions.

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.

Abstract: Some aspects of the present disclosure relate to accelerated imaging using variable-density sampling and compressed sensing with parallel imaging. In one embodiment, a method includes acquiring magnetic resonance data associated with a physiological activity in an area of interest of a subject. The acquiring includes performing accelerated variable-density sampling with phase-contrast displacement encoding. The method also includes reconstructing, from the acquired magnetic resonance data, images corresponding to the physiological activity in the area of interest. The reconstructing includes performing parallel imaging and compressed sensing.

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: 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.

Abstract: Systems and methods for accelerated arterial spin labeling (ASL) using compressed sensing are disclosed. In one aspect, in accordance with one example embodiment, a method includes acquiring magnetic resonance data associated with an area of interest of a subject, wherein the area of interest corresponds to one or more physiological activities of the subject. The method also includes performing image reconstruction using temporally constrained compressed sensing reconstruction on at least a portion of the acquired magnetic resonance data, wherein acquiring the magnetic resonance data includes receiving data associated with ASL of the area of interest of the subject.

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: Systems and methods for accelerated arterial spin labeling (ASL) using compressed sensing are disclosed. In one aspect, in accordance with one example embodiment, a method includes acquiring magnetic resonance data associated with an area of interest of a subject, wherein the area of interest corresponds to one or more physiological activities of the subject. The method also includes performing image reconstruction using temporally constrained compressed sensing reconstruction on at least a portion of the acquired magnetic resonance data, wherein acquiring the magnetic resonance data includes receiving data associated with ASL of the area of interest of the subject.

Abstract: Some aspects of the present disclosure relate to systems and methods for accelerated dynamic magnetic resonance imaging (MRI). In an example embodiment, a method includes acquiring undersampled MRI data corresponding to a set of images associated with an area of interest of a subject, and separating an image of the set of images into image regions. The method also includes performing motion tracking for each of the image regions, grouping the motion-tracked image regions into clusters, and applying a sparsity transform to the clusters, to form sparsity-exploited, transformed image regions. The method further includes forming a set of merged images from the plurality of sparsity-exploited, transformed image regions, and updating the set of merged images based on data fidelity, to form an updated set of estimated images.

Abstract: Systems and methods for accelerated arterial spin labeling (ASL) using compressed sensing are disclosed. In one aspect, in accordance with one example embodiment, a method includes acquiring magnetic resonance data associated with an area of interest of a subject, wherein the area of interest corresponds to one or more physiological activities of the subject. The method also includes performing image reconstruction using temporally constrained compressed sensing reconstruction on at least a portion of the acquired magnetic resonance data, wherein acquiring the magnetic resonance data includes receiving data associated with ASL of the area of interest of the subject.