A polynomial fitting improved Bayesian reconstruction method for whole brain volumetric MRSI metabolite images

Yufang Bao, Andrew Maudsley

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)1-8
Number of pages8
JournalOpen Medical Imaging Journal
Volume7
Issue number1
DOIs
StatePublished - 2013

Keywords

  • Bayesian image reconstruction
  • MRSI k-space data
  • Polynomial fitting
  • Volumetric metabolite images

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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