Reconstruction of MRI data encoded by multiple nonbijective curvilinear magnetic fields

Area of Science:

• Magnetic Resonance Imaging
• Medical Physics
• Biomedical Engineering

Background:

• Parallel imaging technique using localized gradients (PatLoc) employs surface gradient coils for spatial encoding.
• Current PatLoc methods face challenges with ambiguous spatial encoding and reduced resolution at the field-of-view center.

Purpose of the Study:

• To present a generalized image reconstruction method for PatLoc imaging.
• To mitigate challenges associated with ambiguous spatial encoding and resolution loss in PatLoc.
• To demonstrate improved image reconstruction using multiple spatial encoding magnetic fields (SEMs).

Main Methods:

• Developed an efficient and generalized image reconstruction algorithm for PatLoc.
• Utilized multiple SEMs, including linear and multipolar fields, to enhance spatial encoding.
• Compared reconstruction results with conventional linear gradients using simulations and experimental data.

Main Results:

• The generalized reconstruction method effectively addresses ambiguities in SEM encoding.
• The use of multiple SEMs, including linear fields, reduces spatial resolution loss at the field-of-view center.
• Reconstructions using the generalized method show significant improvements compared to conventional techniques.

Conclusions:

• PatLoc systems offer greater flexibility in spatial encoding than previously realized.
• The proposed generalized reconstruction method enhances the performance and applicability of PatLoc MRI.
• Combining multiple SEMs is a viable strategy for improving image reconstruction in PatLoc.