Eigenvector-based SPIRiT Parallel MR Imaging Reconstruction based on ℓ_p pseudo-norm Joint Total Variation

Area of Science:

• Medical Imaging
• Biophysics
• Signal Processing

Background:

• Parallel Magnetic Resonance (MR) imaging accelerates data acquisition using array receiver sensitivities.
• Eigenvector-based SPIRiT (ESPIRiT) is an advanced parallel MR reconstruction method integrating SENSE and GRAPPA advantages.
• ESPIRiT estimates sensitivity maps from calibration data for improved image reconstruction.

Purpose of the Study:

• To enhance parallel MR imaging reconstruction quality.
• To introduce Total Variation (TV) and ℓ_p pseudo-norm Joint TV (ℓ_pJTV) regularization into the ESPIRiT model.
• To evaluate the performance of novel regularization techniques against conventional methods.

Main Methods:

• The Operator Splitting (OS) method was employed to solve the reconstruction problem.
• Majorization Minimization was utilized to address denoising subproblems with TV and ℓ_pJTV regularization.
• Simulation experiments were conducted on two in vivo datasets.

Main Results:

• The proposed OS algorithm with TV regularization (OSTV) and OS with ℓ_pJTV regularization (OSℓ_pJTV) demonstrated superior Signal-to-Noise Ratio (SNR) and Normalized Root Mean Square Error (NRMSE) compared to ℓ₁ regularization.
• The OSℓ_pJTV algorithm showed a slight performance advantage over the OSTV algorithm.
• Both novel methods improved image quality in parallel MR imaging reconstruction.

Conclusions:

• The integration of TV and ℓ_pJTV regularization significantly improves parallel MR imaging reconstruction within the ESPIRiT framework.
• The proposed Operator Splitting approach effectively handles these regularization terms.
• The OSℓ_pJTV method offers a promising advancement for high-quality, accelerated MR imaging.