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BioMed Research International
Volume 2017, Article ID 9016826, 8 pages
Research Article

Instrument Variables for Reducing Noise in Parallel MRI Reconstruction

1Computer Science and Engineering Technology Department, University of Houston-Downtown, Houston, TX 77002, USA
2Massachusetts General Hospital, Charlestown, MA 02129, USA
3Harvard Medical School, Boston, MA 02115, USA
4School of Information Science and Engineering, Institute of Life Sciences, Key Laboratory of Intelligent Information Processing, Shandong Normal University, Jinan 250014, China

Correspondence should be addressed to Yuanjie Zheng; moc.liamg@noisiv.gnehz

Received 25 August 2016; Revised 26 November 2016; Accepted 12 December 2016; Published 19 January 2017

Academic Editor: Jiun-Jie Wang

Copyright © 2017 Yuchou Chang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Generalized autocalibrating partially parallel acquisition (GRAPPA) has been a widely used parallel MRI technique. However, noise deteriorates the reconstructed image when reduction factor increases or even at low reduction factor for some noisy datasets. Noise, initially generated from scanner, propagates noise-related errors during fitting and interpolation procedures of GRAPPA to distort the final reconstructed image quality. The basic idea we proposed to improve GRAPPA is to remove noise from a system identification perspective. In this paper, we first analyze the GRAPPA noise problem from a noisy input-output system perspective; then, a new framework based on errors-in-variables (EIV) model is developed for analyzing noise generation mechanism in GRAPPA and designing a concrete method—instrument variables (IV) GRAPPA to remove noise. The proposed EIV framework provides possibilities that noiseless GRAPPA reconstruction could be achieved by existing methods that solve EIV problem other than IV method. Experimental results show that the proposed reconstruction algorithm can better remove the noise compared to the conventional GRAPPA, as validated with both of phantom and in vivo brain data.