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Correlation between the temperature dependence of intrinsic MR parameters and thermal dose measured by a rapid chemical shift imaging technique.

Authors :
Taylor BA
Elliott AM
Hwang KP
Hazle JD
Stafford RJ
Source :
NMR in biomedicine [NMR Biomed] 2011 Dec; Vol. 24 (10), pp. 1414-21. Date of Electronic Publication: 2011 Jul 01.
Publication Year :
2011

Abstract

In order to investigate simultaneous MR temperature imaging and direct validation of tissue damage during thermal therapy, temperature-dependent signal changes in proton resonance frequency (PRF) shifts, R(2)* values, and T1-weighted amplitudes are measured from one technique in ex vivo tissue. Using a multigradient echo acquisition and the Stieglitz-McBride algorithm, the temperature sensitivity coefficients of these parameters are measured in each tissue at high spatiotemporal resolutions (1.6 x 1.6 x 4 mm 3,≤ 5sec) at the range of 25-61 °C. Non-linear changes in MR parameters are examined and correlated with an Arrhenius rate dose model of thermal damage. Using logistic regression, the probability of changes in these parameters is calculated as a function of thermal dose to determine if changes correspond to thermal damage. Temperature sensitivity of R(2)* and, in some cases, T1-weighted amplitudes are statistically different before and after thermal damage occurred. Significant changes in the slopes of R(2)* as a function of temperature are observed. Logistic regression analysis shows that these changes could be accurately predicted using the Arrhenius rate dose model (Ω = 1.01 ± 0.03), thereby showing that the changes in R(2)* could be direct markers of protein denaturation. Overall, by using a chemical shift imaging technique with simultaneous temperature estimation, R(2)* mapping and T1-W imaging, it is shown that changes in the sensitivity of R(2)* and, to a lesser degree, T1-W amplitudes are measured in ex vivo tissue when thermal damage is expected to occur. These changes could possibly be used for direct validation of thermal damage in contrast to model-based predictions.<br /> (Copyright © 2011 John Wiley & Sons, Ltd.)

Details

Language :
English
ISSN :
1099-1492
Volume :
24
Issue :
10
Database :
MEDLINE
Journal :
NMR in biomedicine
Publication Type :
Academic Journal
Accession number :
21721063
Full Text :
https://doi.org/10.1002/nbm.1707