Abstract

Background The SAturation-recovery single-SHot Acquisition (SASHA) T1 mapping sequence has excellent accuracy independent of T1, T2, heart rate, and flip angle [1], which are known dependencies of the more commonly used MOdified Look-Locker Inversion-recovery (MOLLI) sequence. However, SASHA has a greater T1 variability (poorer precision) compared to MOLLI. A two-parameter fit, with assumed ideal saturation, has been shown to improve precision compared to the standard three-parameter fit used for SASHA, but at the expense of introducing systematic errors [2]. We propose that a variable flip angle (VFA) readout will reduce these systematic errors and thereby allow the improved precision of a two-parameter fit while maintaining the accuracy of the three-parameter fit.

Highlights

  • The SAturation-recovery single-SHot Acquisition (SASHA) T1 mapping sequence has excellent accuracy independent of T1, T2, heart rate, and flip angle [1], which are known dependencies of the more commonly used MOdified Look-Locker Inversion-recovery (MOLLI) sequence

  • Values are reported as mean ± standard deviation across subjects

  • Image artifacts from the bSSFP readout were consistently reduced with the SASHA-variable flip angle (VFA) sequence compared to the standard SASHA sequence, which may contribute to the improved variability performance (Figure 1)

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Summary

Background

The SAturation-recovery single-SHot Acquisition (SASHA) T1 mapping sequence has excellent accuracy independent of T1, T2, heart rate, and flip angle [1], which are known dependencies of the more commonly used MOdified Look-Locker Inversion-recovery (MOLLI) sequence. SASHA has a greater T1 variability (poorer precision) compared to MOLLI. A two-parameter fit, with assumed ideal saturation, has been shown to improve precision compared to the standard three-parameter fit used for SASHA, but at the expense of introducing systematic errors [2]. We propose that a variable flip angle (VFA) readout will reduce these systematic errors and thereby allow the improved precision of a two-parameter fit while maintaining the accuracy of the three-parameter fit

Methods
Results
Conclusions
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