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Comparison of reconstruction accuracy and efficiency among autocalibrating data‐driven parallel imaging methods
Author(s) -
Brau Anja C.S.,
Beatty Philip J.,
Skare Stefan,
Bammer Roland
Publication year - 2008
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.21481
Subject(s) - computer science , cartesian coordinate system , calibration , task (project management) , process (computing) , iterative reconstruction , domain (mathematical analysis) , phase (matter) , algorithm , class (philosophy) , artificial intelligence , data mining , mathematics , mathematical analysis , statistics , chemistry , geometry , management , organic chemistry , economics , operating system
Abstract The class of autocalibrating “data‐driven” parallel imaging (PI) methods has gained attention in recent years due to its ability to achieve high quality reconstructions even under challenging imaging conditions. The aim of this work was to perform a formal comparative study of various data‐driven reconstruction techniques to evaluate their relative merits for certain imaging applications. A total of five different reconstruction methods are presented within a consistent theoretical framework and experimentally compared in terms of the specific measures of reconstruction accuracy and efficiency using one‐dimensional (1D)‐accelerated Cartesian datasets. It is shown that by treating the reconstruction process as two discrete phases, a calibration phase and a synthesis phase, the reconstruction pathway can be tailored to exploit the computational advantages available in certain data domains. A new “split‐domain” reconstruction method is presented that performs the calibration phase in k ‐space ( k x , k y ) and the synthesis phase in a hybrid ( x , k y ) space, enabling highly accurate 2D neighborhood reconstructions to be performed more efficiently than previously possible with conventional techniques. This analysis may help guide the selection of PI methods for a given imaging task to achieve high reconstruction accuracy at minimal computational expense. Magn Reson Med 59:382–395, 2008. © 2008 Wiley‐Liss, Inc.