UNFOLD-SENSE: a parallel MRI method with self-calibration and artifact suppression

Area of Science:

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

Background:

• Parallel imaging accelerates MRI acquisition but often suffers from artifacts and noise, particularly at higher acceleration factors.
• Existing methods like TSENSE and GRAPPA have limitations in sensitivity mapping and artifact suppression.
• The need for faster and more robust MRI techniques is critical for clinical applications.

Purpose of the Study:

• To enhance parallel imaging performance by integrating the "unaliasing by Fourier-encoding the overlaps in the temporal dimension" (UNFOLD) strategy with a new self-calibration method.
• To introduce a novel self-calibration technique, "self, hybrid referencing with UNFOLD and GRAPPA" (SHRUG), for fast calibration at any acceleration factor.
• To develop and validate the UNFOLD-SENSE method for improved artifact and noise suppression in accelerated MRI.

Main Methods:

• Developed SHRUG by merging UNFOLD-based sensitivity mapping with GRAPPA strategies to overcome individual limitations.
• Integrated an UNFOLD artifact suppression scheme into UNFOLD-SENSE for enhanced noise and artifact reduction.
• Introduced variable-density SENSE (vdSENSE) to enable reconstruction of variable-density data with Cartesian SENSE simplicity.
• Combined SHRUG, vdSENSE, and UNFOLD artifact suppression into the complete UNFOLD-SENSE method.

Main Results:

• SHRUG provides fast self-calibration applicable to any acceleration factor.
• The UNFOLD artifact suppression scheme demonstrated superior performance at accelerations > 2.0, achieving up to double the artifact suppression compared to previous methods.
• vdSENSE allowed for efficient reconstruction of data sampled with variable density.
• UNFOLD-SENSE was successfully implemented with online reconstruction for SSFP and myocardium-perfusion sequences.

Conclusions:

• UNFOLD-SENSE effectively combines SHRUG, vdSENSE, and UNFOLD artifact suppression for improved parallel MRI.
• The method significantly suppresses artifacts and amplified noise, particularly at high acceleration factors.
• UNFOLD-SENSE shows promise for faster and more accurate MRI acquisitions in clinical settings, as evidenced by patient scans.