System and method for deep learning-based ultrafast and robust rf shimming using quadrature b1+ maps

A system for performing radio frequency shimming, comprising:

• a memory encoding processor-executable routines;
• and
• a processing system comprising one or more processors and configured to access the memory and to execute the processor-executable routines, wherein the processor-executable routines, when executed by the processing system, cause the processing system to:
• obtain quadrature B1+ maps of an imaging volume of a subject;
• input the quadrature B1+ maps into a deep learning-based radio frequency shimming inference model;
• output from the deep learning-based radio frequency shimming inference model radio frequency shims based on the quadrature B1+ maps;
• and
• apply the radio frequency shims to a magnetic resonance imaging scanner prior to a parallel transmission diagnostic scan of the imaging volume of the subject.

A non-transitory computer-readable medium, the computer-readable medium comprising processor-executable code that when executed by a processing system comprising one or more processors, causes the processing system to:

• obtain quadrature B1+ maps of an imaging volume of a subject;
• input the quadrature B1+ maps into a deep learning-based radio frequency shimming inference model;
• output from the deep learning-based radio frequency shimming inference model radio frequency shims based on the quadrature B1+ maps;
• and
• apply the radio frequency shims to a magnetic resonance imaging scanner prior to a parallel transmission diagnostic scan of the imaging volume of the subject.