The Open Medical Imaging Journal

2013, 7 : 1-8
Published online 2013 February 7. DOI: 10.2174/1874347101307010001
Publisher ID: TOMIJ-7-1

A Polynomial Fitting Improved Bayesian Reconstruction Method for Whole Brain Volumetric MRSI Metabolite Images

Yufang Bao and Andrew Maudsley
Department of Mathematics and Computer Science, UNC Fayetteville State University, Fayetteville, NC 28301.

ABSTRACT

In this paper, a polynomial fitting improved Bayesian approach is proposed for the reconstruction of volumetric metabolite images from long echo time (TE) whole brain proton magnetic resonance spectroscopic imaging (MRSI) data. The proposed algorithm uses a modified EM (expectation maximization) algorithm that takes into account the partial volume effects contained inside a thick slice MRSI. It incorporates high resolution volumetric magnetic resonance imaging (MRI) as a priori information. It further integrates the polynomial fitting method to smooth out artificial edges before the high resolution metabolite images are reconstructed. Our proposed reconstruction method has successfully extended our existing reconstruction of two dimensional (2D) metabolite images to 3D cases. The experimental results show that resolution enhanced volumetric metabolite images are reconstructed.

Keywords:

MRSI k-space data, volumetric metabolite images, Bayesian image reconstruction.