System and method for scan time reduction for propeller magnetic resonance imaging acquisition using deep learning reconstruction

A system for reducing scan time of periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) imaging, comprising:

• a memory encoding processor-executable routines;
• and
• a processor configured to access the memory and to execute the processor-executable routines, wherein the processor-executable routines, when executed by the processor, cause the processor to:
• acquire, in accelerated manner, a plurality of blades of k-space data of a region of interest in a rotational manner around a center of k-space via a magnetic resonance imaging (MRI) scanner during a PROPELLER sequence, wherein each blade of the plurality of blades of k-space data comprises a plurality of parallel phase encoding lines sampled in a phase encoding order, and wherein each blade of the plurality of blades of k-space data is undersampled;
• utilize a deep learning-based Cartesian-like reconstruction network to individually and separately reconstruct each blade of the plurality of blades of k-space data to generate a plurality of fully sampled blades;
• and
• utilize a PROPELLER reconstruction algorithm to generate a complex image from the plurality of fully sampled blades.

A non-transitory computer-readable medium, the non-transitory computer-readable medium comprising processor-executable code that when executed by a processor, causes the processor to:

• acquire, in an accelerated manner, a plurality of blades of k-space data of a region of interest in a rotational manner around a center of k-space via a magnetic resonance imaging (MRI) scanner during a periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) sequence, wherein each blade of the plurality of blades of k-space data comprises a plurality of parallel phase encoding lines sampled in a phase encoding order, and wherein each blade of the plurality of blades of k-space data is undersampled;
• utilize a deep learning-based Cartesian-like reconstruction network to individually and separately reconstruct each blade of the plurality of blades of k-space data to generate a plurality of fully sampled blades;
• and
• utilize a PROPELLER reconstruction algorithm to generate a complex image from the plurality of fully sampled blades.