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Criteria for analysis of multicomponent tissue T 2 relaxation data
Author(s) -
Graham Simon J.,
Stanchev Peter L.,
Bronskill Michael J.
Publication year - 1996
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.1910350315
Subject(s) - measure (data warehouse) , monte carlo method , relaxation (psychology) , volume (thermodynamics) , partial volume , t2 relaxation , basis (linear algebra) , pixel , noise (video) , nuclear magnetic resonance , computer science , biological system , mathematics , physics , statistics , magnetic resonance imaging , artificial intelligence , data mining , medicine , image (mathematics) , radiology , geometry , quantum mechanics , biology
Monte Carlo simulations were performed to determine whether the multicomponent T 2 distribution of tissue can be estimated accurately from T 2 decay data acquired in vivo. Simulated data were generated for white matter, fast twitch muscle, and breast tissue. The signal‐to‐noise ratio, number of data samples, and minimum echo time were varied from the experimental conditions currently achievable with MRI to those achievable for in vitro experiments. Data were fitted by a distribution of T 2 values using the T2NNLS algorithm, and statistics characterizing the estimated T 2 components were determined. Current MRI techniques were found to provide conditions insufficient for accurate multicomponent T 2 analysis on a pixel‐by‐pixel basis. However, volume localization methods that measure T 2 decay from a large volume of interest have potential for this analysis. These results illustrate a general framework for development of new techniques to measure T 2 decay accurately in vivo.