Deep learning techniques for magnetic resonance image reconstruction

At least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform a method comprising:

• generating a magnetic resonance (MR) image from input MR spatial frequency data using a neural network model comprising one or more neural network blocks including a first neural network block, wherein the first neural network block is configured to perform processing using a non-uniform Fourier transformation for transforming image domain data to spatial frequency domain data;
• and
• applying the first neural network block to image domain data, wherein the applying comprises:
• applying, to the image domain data, the non-uniform Fourier transformation followed by an adjoint non-uniform Fourier transformation to obtain first output;
• applying the adjoint non-uniform Fourier transformation to the input MR spatial frequency data to obtain second output;
• and
• providing the image domain data, the first output, and the second output as inputs to a plurality of convolutional layers.

A magnetic resonance imaging (MRI) system, comprising:

• a magnetics system comprising:
• a B0 magnet configured to provide a B0 field for the MRI system;
• gradient coils configured to provide gradient fields for the MRI system;
• at least one RF coil configured to detect magnetic resonance (MR) signals;
• and
• a controller configured to:
• control the magnetics system to acquire MR spatial frequency data using a non-Cartesian sampling trajectory;
• generate an MR image from the acquired MR spatial frequency data using a neural network model comprising one or more neural network blocks including a first neural network block, wherein the first neural network block is configured to perform processing using a non-uniform Fourier transformation;
• and
• apply the first neural network block to image domain data, wherein the applying comprises:
• applying, to the image domain data, the non-uniform Fourier transformation followed by an adjoint non-uniform Fourier transformation to obtain first output;
• applying the adjoint non-uniform Fourier transformation to the input MR spatial frequency data to obtain second output;
• and
• providing the image domain data, the first output, and the second output as inputs to a plurality of convolutional layers.