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• W2897627818 abstract "Abstract Purpose In this work we demonstrate how sequence parameter settings influence the accuracy and precision in T 1 , T 2 , and off‐resonance maps obtained with the PLANET method for a single‐component signal model. In addition, the performance of the method for the particular case of a two‐component relaxation model for white matter tissue was assessed. Methods Numerical simulations were performed to investigate the influence of sequence parameter settings on the accuracy and precision in the estimated parameters for a single‐component model, as well as for a two‐component white matter model. Phantom and in vivo experiments were performed for validation. In addition, the effects of Gibbs ringing were investigated. Results By making a proper choice for sequence parameter settings, accurate and precise parameter estimation can be achieved for a single‐component signal model over a wide range of relaxation times at realistic SNR levels. Due to the presence of a second myelin‐related signal component in white matter, an underestimation of approximately 30% in T 1 and T 2 was observed, predicted by simulations and confirmed by measurements. Gibbs ringing artifacts correction improved the precision and accuracy of the parameter estimates. Conclusion For a single‐component signal model there is a broad “sweet spot” of sequence parameter combinations for which a high accuracy and precision in the parameter estimates is achieved over a wide range of relaxation times. For a multicomponent signal model, the single‐component PLANET reconstruction results in systematic errors in the parameter estimates as expected." @default.
• W2897627818 created "2018-10-26" @default.
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• W2897627818 date "2018-10-10" @default.
• W2897627818 modified "2023-10-16" @default.
• W2897627818 title "On the accuracy and precision of PLANET for multiparametric MRI using phase‐cycled bSSFP imaging" @default.
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• W2897627818 doi "https://doi.org/10.1002/mrm.27491" @default.
• W2897627818 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6585657" @default.
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• W2897627818 hasPublicationYear "2018" @default.
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