Deep learning based magnetic resonance imaging (MRI) examination acceleration

A method of deep learning (DL) based magnetic resonance imaging (MRI) examination acceleration, the method comprising:

• acquiring at least one fully sampled reference k-space data of a subject;
• acquiring a plurality of partial k-space data of the subject;
• grafting the plurality of partial k-space data with the at least one fully sampled reference k-space data to generate a grafted data for accelerated examination;
• and
• training a deep learning (DL) module using the grafted data and the fully sampled reference k-space data to remove grafting artifacts.

A magnetic resonance imaging (MRI) system comprising:

• at least one radiofrequency (RF) body coil adapted to transmit and receive radiofrequency (RF) signals to and from a subject;
• a transceiver module configured to digitize the signals received by the radiofrequency (RF) body coil;
• a control system configured to process the digitized signals and generate a k-space data corresponding to an imaged volume of the subject, wherein the MRI system is configured to acquire fully sampled reference k-space data of the subject and a plurality of partial k-space data of the subject;
• a computer processor configured to graft the plurality of partial k-space data of the subject with the fully sampled reference k-space data of the subject to generate a grafted k-space data for accelerated examination;
• and
• wherein the MRI system further comprises a deep learning (DL) module employed on a computer memory, wherein the deep learning (DL) module is trained using the fully sampled reference k-space data and the grafted k-space data.