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CORE ‐ PI : Non‐iterative convolution‐based reconstruction for parallel MRI in the wavelet domain
Author(s) -
Shimron Efrat,
Webb Andrew G.,
Azhari Haim
Publication year - 2019
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.13260
Subject(s) - undersampling , wavelet , iterative reconstruction , algorithm , convolution (computer science) , imaging phantom , reconstruction filter , filter (signal processing) , mathematics , curvelet , computer science , wavelet transform , artificial intelligence , computer vision , physics , artificial neural network , filter design , optics , root raised cosine filter
Purpose To develop and test a novel parameter‐free non‐iterative wavelet domain method for reconstruction of undersampled multicoil MR data. Theory and Methods A linear parallel MRI method that operates in the Stationary Wavelet Transform ( SWT ) domain is proposed. The method is coined CO nvolution‐based RE construction for Parallel MRI ( CORE ‐ PI ). This method computes the SWT of the unknown MR image directly from subsampled k‐space measurements, without modifying the RF excitation pulse. It then reconstructs the image using the wavelet filter bank approach, with simple linear computations. The CORE ‐ PI implementation is demonstrated by experiments with a numeric brain phantom and in vivo brain scans data, with various wavelet types and high reduction factors. It is compared to the well‐known parallel MRI methods GRAPPA and l 1‐ SPIR iT. Results The experimental results show that CORE ‐ PI is suitable for different 1D Cartesian k‐space undersampling schemes, including regular and irregular ones, and for wavelets of different families. CORE ‐ PI accurately reconstructs the SWT coefficients of the unknown MR image; this wavelet‐domain decomposition is fully computed despite the k‐space undersampling. Furthermore, CORE ‐ PI provides high‐quality final reconstructions, with an average NRMSE of 0.013, which is significantly lower than that obtained by GRAPPA and l 1‐ SPIR iT. Moreover, CORE ‐ PI offers significantly faster computation times: the typical CORE ‐ PI runtime is about 60 seconds, which is about 20% shorter than that of l 1‐ SPIR iT and 55%–75% shorter than that of GRAPPA . Conclusion COnvolution‐based REconstruction for Parallel MRI advantageously offers: (a) flexible 1D undersampling of a Cartesian k‐space, (b) a parameter‐free non‐iterative implementation, (c) reconstruction performance comparable or better than that of GRAPPA and l 1‐ SPIR iT, and (d) robust fast computations.