Instrument Variables for Reducing Noise in Parallel MRI Reconstruction.

Yuchou Chang,Haifeng Wang,Hong Lin,Yuanjie Zheng

doi:10.1155/2017/9016826

Abstract

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.

Highlights

Over the past few years, generalized autocalibrating partially parallel acquisition (GRAPPA) [1], as an efficient parallel magnetic resonance imaging technique, has been widely studied
GRAPPA reconstruction with the same net reduction factor is considered for comparison, in which a lower reduction factor of 3 and less number of autocalibration signal (ACS) lines are used
We can see that, at reduction factor 4, noises are generated by GRAPPA reconstruction, which seriously deteriorate image quality

Summary

Introduction

Over the past few years, generalized autocalibrating partially parallel acquisition (GRAPPA) [1], as an efficient parallel magnetic resonance imaging (pMRI) technique, has been widely studied. BioMed Research International square [7] These methods tried to suppress noise in estimating weight process rather than from system identification perspective. Since GRAPPA reconstruction has been viewed as a linear system [1, 11], in which ACS and a part of acquired data construct the input and output of the system in fitting process, based on the observation that input and output have been contaminated by noise from scanner, estimation of GRAPPA modeling weights will be biased, whose severity depends on measured noise power. This article presents a framework based on the EIV model for identifying true weights of GRAPPA reconstruction Under this framework, a concrete method—IV GRAPPA— is proposed, which discovers true functional relationship among sampled and missing k-space signals in terms of accurate fitting weights. We provide theoretical foundation and mathematical description of the proposed method, and based on which, a set of representative experimental results and our discussions are presented

Theory and Method

Experiments and Results

Conclusion