title: NOVIFAST: A Fast Algorithm for Accurate and Precise VFA MRI T1 Mapping
creator: Ramos Llordén, Gabriel
creator: Vegas Sánchez-Ferrero, Gonzalo
creator: Björk, Marcus
creator: Vanhevel, Floris
creator: Parizel, Paul M.
creator: San José Estépar, Raúl
creator: Dekker, Arnold J. den
creator: Sijbers, Jan
subject: Telecommunications
description: In quantitative magnetic resonance T1 mapping, the variable flip angle (VFA) steady state spoiled gradient recalled echo (SPGR) imaging technique is popular as it provides a series of high resolution T1 weighted images in a clinically feasible time. Fast, linear methods that estimate T1 maps from these weighted images have been proposed, such as DESPOT1 and iterative re-weighted linear least squares. More accurate, non-linear least squares (NLLS) estimators are in play, but these are generally much slower and require careful initialization. In this paper, we present NOVIFAST, a novel NLLS-based algorithm specifically tailored to VFA SPGR T1 mapping. By exploiting the particular structure of the SPGR model, a computationally efficient, yet accurate and precise T1 map estimator is derived. Simulation and in vivo human brain experiments demonstrate a twenty-fold speed gain of NOVIFAST compared with conventional gradient-based NLLS estimators while maintaining a high precision and accuracy. Moreover, NOVIFAST is eight times faster than the efficient implementations of the variable projection (VARPRO) method. Furthermore, NOVIFAST is shown to be robust against initialization.
publisher: E.T.S.I. Telecomunicación (UPM)
rights: https://creativecommons.org/licenses/by-nc-nd/3.0/es/
date: 2018-11
type: info:eu-repo/semantics/article
type: Article
source: IEEE transactions on medical imaging, ISSN 0278-0062, 2018-11, Vol. 37, No. 11
type: PeerReviewed
format: application/pdf
language: eng
relation: https://ieeexplore.ieee.org/document/8371285
relation: info:eu-repo/grantAgreement/EC/FP7/291820
rights: info:eu-repo/semantics/openAccess
relation: info:eu-repo/semantics/altIdentifier/doi/10.1109/TMI.2018.2833288
identifier: https://oa.upm.es/55074/