Image reconstruction for the rotating RF coil using k-t bin robust principal component analysis (RPCA) method.

Area of Science:

• Medical Imaging
• Magnetic Resonance Imaging (MRI)
• Signal Processing

Background:

• Phased-array coils are standard in MRI, but rotating radiofrequency coils (RRFC) offer an alternative.
• Current RRFC reconstruction methods often require detailed coil sensitivity information, limiting their application.
• Artifacts arise in RRFC due to Fourier invariant violation from coil rotation, complicating image reconstruction.

Purpose of the Study:

• To develop a novel image reconstruction algorithm for RRFC that does not require coil sensitivity information.
• To address and remove artifacts present in RRFC-based MR scans.
• To improve the quality and applicability of RRFC in magnetic resonance imaging.

Main Methods:

• A novel reconstruction algorithm employing Robust Principal Component Analysis (RPCA) was developed.
• The algorithm incorporates a k-space-time (k-t) sparse bin reformation method (rotating k-t bin).
• The method iteratively removes artifacts in temporal and frequency domains using a golden angle (GA) radial k-space and stepping-mode coil rotation data sampling scheme.

Main Results:

• The proposed RPCA-based algorithm successfully reconstructs images without relying on coil sensitivity data.
• Artifacts caused by coil rotation were effectively removed in both temporal and frequency domains.
• Simulation results validated the effectiveness of the novel imaging method for RRFC scans.

Conclusions:

• The presented RPCA with rotating k-t bin method offers a viable solution for RRFC image reconstruction.
• This approach eliminates the need for explicit coil sensitivity mapping, simplifying the process.
• The method demonstrates significant potential for enhancing RRFC-based MRI applications.