On the accuracy and precision of PLANET for multiparametric MRI using phase-cycled bSSFP imaging.
Magn Reson Med
; 81(3): 1534-1552, 2019 03.
Article
em En
| MEDLINE
| ID: mdl-30303562
ABSTRACT
PURPOSE:
In this work we demonstrate how sequence parameter settings influence the accuracy and precision in T1 , T2 , 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 T1 and T2 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.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Processamento de Imagem Assistida por Computador
/
Encéfalo
/
Substância Branca
/
Imageamento por Ressonância Magnética Multiparamétrica
/
Bainha de Mielina
Tipo de estudo:
Health_economic_evaluation
/
Prognostic_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2019
Tipo de documento:
Article