Abstract
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In quantitative magnetic resonance T1 mapping, the variable ?ip 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 speci?cally tailored to VFA SPGR T1 mapping. By exploiting the particular structure of the SPGR model, a computationally ef?cient, yet accurate and precise T1 map estimator is derived. Simulation and i n v i vo 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 ef?cient implementations of the variable projection (VARPRO) method. Furthermore, NOVIFAST is shown to be robust against initialization. | |
International
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Si |
JCR
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Si |
Title
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Ieee Transactions on Medical Imaging |
ISBN
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0278-0062 |
Impact factor JCR
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6,131 |
Impact info
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Volume
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37 |
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10.1109/TMI.2018.2833288 |
Journal number
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11 |
From page
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2414 |
To page
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2427 |
Month
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NOVIEMBRE |
Ranking
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