2D partially parallel imaging with k-space surrounding neighbors-based data reconstruction.

Area of Science:

• Magnetic Resonance Imaging (MRI)
• Medical Imaging Technology
• Image Reconstruction Algorithms

Background:

• Partially parallel imaging (PPI) accelerates MRI by using receiver surface coil arrays to replace partial phase encoding.
• Two-dimensional (2D) PPI in 3D MRI offers further acceleration potential by utilizing two phase encoding directions.
• Existing PPI methods require exploration for optimal k-space strategies in 3D contexts.

Purpose of the Study:

• To explore k-space-based acquisition and reconstruction strategies for 2D partially parallel imaging (PPI) in 3D MRI.
• To present novel methods for accelerating 3D MRI acquisition using PPI techniques.
• To evaluate the performance of proposed PPI strategies using both simulated and in vivo data.

Main Methods:

• Introduced a surrounding neighbors-based autocalibrating PPI (SNAPPI) method, generalizing 2D interpolation techniques.
• Developed several 2D PPI reconstruction methods applying SNAPPI to recover undersampled k-space data in 2D or 3D MRI.
• Presented an optimal 2D PPI sampling-based reconstruction for directions with limited coil sensitivity variation.

Main Results:

• Demonstrated the efficacy of SNAPPI for autocalibration in PPI.
• Showcased various 2D PPI reconstruction approaches for accelerated 3D MRI data acquisition.
• Validated the proposed methods using both simulated datasets and in vivo 2D PPI data.

Conclusions:

• The explored k-space-based strategies, including SNAPPI, provide effective means for accelerating 3D MRI acquisition.
• The developed reconstruction methods offer flexibility and improved performance for 2D PPI in 3D MRI.
• The findings support the advancement of faster and more efficient 3D MRI protocols.