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Susceptibility‐related MR signal dephasing under nonstatic conditions: Experimental verification and consequences for qBOLD measurements
Author(s) -
Sohlin Maja C.,
Schad Lothar R.
Publication year - 2011
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.22423
Subject(s) - dephasing , signal (programming language) , imaging phantom , diffusion , estimation theory , stability (learning theory) , biological system , physics , voxel , diffusion mri , observational error , nuclear magnetic resonance , computational physics , statistical physics , mathematics , algorithm , computer science , optics , statistics , magnetic resonance imaging , thermodynamics , condensed matter physics , artificial intelligence , medicine , radiology , machine learning , biology , programming language
Purpose To experimentally verify a theoretical model describing the MR signal dephasing under nonstatic conditions in a voxel containing a vascular network, and to estimate the stability of the model for qBOLD measurements. Materials and Methods Measurement phantoms reflecting the properties of the theoretical model, i.e., statistically distributed and randomly oriented cylinders in a homogeneous medium were constructed by randomly coiled polyamide fibers immersed in a NiSO 4 solution. The resemblance between measured and theoretical signal curves was investigated by calculation of root mean squared error maps. Simulated nonstatic dephasing data were evaluated using the static dephasing model to estimate the stability of the model and the influence of input parameters. Results The theoretical model describing the MR signal dephasing under nonstatic conditions was experimentally verified in phantom measurements. In simulations, it was found that, by neglecting the effect of diffusion when predicting the MR signal‐time course expected in an in vivo measurement of the tissue oxygenation, errors of 10–30% would be introduced into the parameter estimation. The simulations indicate unpredictable results for simultaneous evaluation of blood oxygenation level and blood volume fraction. Conclusion Neglecting the effects of diffusion in quantitative BOLD measurements could give rise to substantial errors in the parameter estimation. J. Magn. Reson. Imaging 2011;33:417–425. © 2011 Wiley‐Liss, Inc.