Regularization in parallel magnetic resonance imaging

摘要

SPIRiT (iterative self-consistent parallel imaging reconstruction) can be solved efficiently for data acquired on arbitrary k-space trajectories, and its sparsity regularized variant L1-SPIRiT accelerates reconstruction. In this paper, we propose a regularized SPIRiT reconstruction based on steerable pyramid decomposition. The directionally filter banks lead to a better separation of signal and noise compared to a discrete wavelet transform (DWT). In vivo datasets and eight-channel Shepp-Logan phantom studies demonstrate efficient reconstructions. We compared our work with five state-of-the-art parallel imaging techniques; our method yields better reconstruction results.