Systems and methods for magnetic resonance image reconstruction

A method for magnetic resonance imaging (MRI) implemented on a computing apparatus, the computing apparatus including at least one processor and at least one storage device, the method comprising:

• obtaining, by the at least one processor, k-space data associated with MR signals acquired by an MR scanner, the k-space data corresponding to a first sampling rate;
• generating, by the at least one processor, one or more estimated images based on the k-space data and a target machine learning model, the one or more estimated images corresponding to a second sampling rate that exceeds the first sampling rate;
• and
• determining, by the at least one processor, one or more target images based on the one or more estimated images and the k-space data using a compressed sensing model, wherein the compressed sensing model is constructed based on the one or more estimated images.

A non-transitory computer-readable medium storing at least one set of instructions, wherein when executed by at least one processor, the at least one set of instructions directs the at least one processor to perform acts of:

• obtaining, by the at least one processor, k-space data associated with MR signals acquired by an MR scanner, the k-space data corresponding to a first sampling rate;
• generating, by the at least one processor, one or more estimated images based on the k-space data and a target neural network model, the one or more estimated images corresponding to a second sampling rate that exceeds the first sampling rate;
• and
• determining, by the at least one processor, one or more target images based on the one or more estimated images and the k-space data using a compressed sensing model, wherein the compressed sensing model is constructed based on the one or more estimated images.