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Regularization in parallel magnetic resonance imaging
Author(s) -
Korti Amel
Publication year - 2018
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.22260
Subject(s) - k space , regularization (linguistics) , imaging phantom , computer science , iterative reconstruction , algorithm , filter (signal processing) , artificial intelligence , discrete wavelet transform , wavelet , signal reconstruction , computer vision , mathematics , signal processing , wavelet transform , fourier transform , physics , optics , mathematical analysis , telecommunications , radar
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.