Comparison of gradient echo and gradient echo sampling of spin echo sequence for the quantification of the oxygen extraction fraction from a combined quantitative susceptibility mapping and quantitative BOLD (QSM+qBOLD) approach. Academic Article uri icon

Overview

abstract

  • PURPOSE: To compare gradient echo (GRE) and gradient echo sampling of spin echo (GESSE) sequences for the quantification of the oxygen extraction fraction (OEF) from combined quantitative BOLD and quantitative susceptibility mapping (QSM) with regard to accuracy, precision and parameter initialization. METHODS: GRE and GESSE data were acquired from 7 healthy volunteers. QSM was applied to the GRE data and used as a regularization for the single-compartment quantitative BOLD fit to the GESSE and GRE data, respectively, to quantify OEF, deoxygenated blood volume (ν), R2 , and non-blood susceptibility (χnb ). Intersubject means within gray and white matter, respectively, were compared between GESSE and GRE (Student's t) and gray-white matter contrast was determined for each sequence separately. A single- and multi-compartment simulation was used to compare reconstruction accuracy. RESULTS: Intersubject means and SDs for gray and white matter were OEF = 32.4 ± 1.6%, ν = 2.9 ± 0.1%, R2 = 14.2 ± 0.5 Hz, χnb = -43 ± 5 ppb for GESSE and OEF = 43.0 ± 5.4%, ν = 3.5 ± 0.4%, R2 = 14.4 ± 0.7 Hz, χnb = -43 ± 8 ppb for GRE with a significant difference (P < 0.05) for OEF and ν. Gray-white matter contrast was significant (P < 0.05) in all parameters for GESSE but only in ν and R2 for GRE. All parameters reconstructed from GESSE had higher accuracy than from GRE in the single- but not multi-compartment simulation. CONCLUSION: GESSE yields higher parameter accuracy in simulated gray matter but produces unphysiological gray-white matter contrast in OEF in vivo. GRE produces uniform OEF maps in vivo and is more efficient, which could facilitate a clinical implementation, but revealed biases in simulation. The appropriate sequence should be chosen depending on application.

publication date

  • June 2, 2019

Research

keywords

  • Image Interpretation, Computer-Assisted
  • Magnetic Resonance Imaging
  • Oxygen

Identity

Scopus Document Identifier

  • 85067013300

Digital Object Identifier (DOI)

  • 10.1002/mrm.27804

PubMed ID

  • 31155754

Additional Document Info

volume

  • 82

issue

  • 4