Systems and methods for phase unwrapping for dense mri using deep learning

A method of using a convolutional neural network (CNN) to calculate a wrapping label map for unwrapping an array of potentially-wrapped measured phase values from frames of magnetic resonance image (MRI) data, the method comprising:

• calculating a phase image for each frame of the displacement encoded MRI data, the phase image comprising potentially-wrapped measured phase values corresponding to pixels (i, j) of the frame;
• training the convolutional neural network with augmented test data to label each pixel (i, j) as belonging to one of three classes, wherein the classes comprise a no-wrap label, a −2π label, and a +2π label;
• and
• storing the respective labels in a wrapping label map.

A system, comprising:

• a data acquisition device configured to acquire displacement encoded magnetic resonance image (MRI) data corresponding to a cardiac region of interest of a subject;
• a computer-implemented convolutional neural network (CNN);
• one or more processors coupled to the data acquisition device and the CNN and configured to cause the system to perform functions that comprise:
• generating a phase image for each frame of the displacement encoded MRI data, wherein the phase image comprises potentially phase-wrapped measured phase values corresponding to pixels of the frame;
• training a convolutional neural network (CNN) to compute a wrapping label map for the phase image, wherein the wrapping label map comprises a respective number of phase wrap cycles present at each pixel in the phase image;
• computing, by the trained CNN, the wrapping label map;
• computing an unwrapped phase image by adding a respective phase correction to each of the potentially-wrapped measured phase values of the phase image, wherein the phase correction is based on the number of phase wrap cycles present at each pixel;
• and
• computing myocardial strain using the unwrapped phase image for strain analysis of the subject.

A non-transitory computer-readable medium storing instructions which, when executed by one or more processors, cause one or more computing devices to perform functions for strain analysis of a cardiac region of interest of a subject from displacement encoded magnetic resonance image (MRI) data, and wherein the performed functions comprise:

• acquiring displacement encoded MRI data corresponding to the cardiac region of interest of the subject;
• generating a phase image for each frame of the displacement encoded MRI data, wherein the phase image comprises potentially phase-wrapped measured phase values corresponding to pixels of the frame;
• training a convolutional neural network (CNN) to compute a wrapping label map for the phase image, wherein the wrapping label map comprises a respective number of phase wrap cycles present at each pixel in the phase image;
• computing, by the trained CNN, the wrapping label map;
• computing an unwrapped phase image by adding a respective phase correction to each of the potentially-wrapped measured phase values of the phase image, wherein the phase correction is based on the number of phase wrap cycles present at each pixel;
• and
• computing myocardial strain using the unwrapped phase image for strain analysis of the subject.