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Data‐driven optimized flip angle selection for T 1 estimation from spoiled gradient echo acquisitions
Author(s) -
Lewis Christina M.,
Hurley Samuel A.,
Meyerand M. Elizabeth,
Koay Cheng Guan
Publication year - 2016
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.25920
Subject(s) - flip angle , range (aeronautics) , set (abstract data type) , selection (genetic algorithm) , algorithm , data set , computer science , variance (accounting) , echo (communications protocol) , mathematics , artificial intelligence , materials science , magnetic resonance imaging , medicine , computer network , accounting , business , composite material , radiology , programming language
Purpose Define criteria for selection of optimal flip angle sets for T 1 estimation and evaluate effects on T 1 mapping. Theory and Methods Flip angle sets for spoiled gradient echo‐based T 1 mapping were selected by minimizing T 1 estimate variance weighted by the joint density of M 0 and T 1 in an initial acquisition. The effect of optimized flip angle selection on T 1 estimate error was measured using simulations and experimental data in the human and rat brain. Results For two‐point acquisitions, optimized angle sets were similar to those proposed by other groups and, therefore, performed similarly. For multipoint acquisitions, optimal angle sets for T 1 mapping in the brain consisted of a repetition of two angles. Implementation of optimal angles reduced T 1 estimate variance by 30–40% compared with a multipoint acquisition using a range of angles. Performance of the optimal angle set was equivalent to that of a repetition of the two‐angle set selected using criteria proposed by other researchers. Conclusion Repetition of two carefully selected flip angles notably improves the precision of resulting T 1 estimates compared with acquisitions using a range of flip angles. This work provides a flexible and widely applicable optimization method of particular use for those who repeatedly perform T 1 estimation. Magn Reson Med 76:792–802, 2016. © 2015 Wiley Periodicals, Inc.