Fast Parallel Imaging Reconstruction Method Based on SIDWT and Iterative Self-Consistency

Abstract

To improve the reconstruction speed of parallel magnetic resonance imaging, an efficient reconstruction method named fSIDWT-SPIRiT is proposed based on shift-invariant discrete wavelets transform (SIDWT) and the iterative self-consistent parallel imaging reconstruction (SPIRiT) model. This method addresses the complex optimization problem containing data consistency term, calibration consistency term, and L1-norm regularization term. First, data consistency term and calibration consistency term are combined and processed, and then solved by a projected fast iterative shrinkage-thresholding algorithm to achieve fast parallel MRI reconstruction. Finally, simulation experiments are conducted using different human organ datasets. The results show that the proposed method is able to guarantee the image reconstruction quality with a faster convergence speed compared with other methods.

Keywords

• parallel magnetic resonance imaging
• iterative self-consistent parallel imaging reconstruction (spirit)
• image reconstruction
• projected fast iterative shrinkage-thresholding algorithm (pfista)
• shift-invariant discrete wavelets transform (sidwt)