Deoni Sean C L, Kolind Shannon H
Advanced Baby Imaging Lab, Brown University School of Engineering, Providence, Rhode Island, USA.
Department of Medicine, University of British Columbia, Vancouver, Canada.
Magn Reson Med. 2015 Jan;73(1):161-9. doi: 10.1002/mrm.25108. Epub 2014 Jan 24.
Multicomponent driven equilibrium single pulse observation of T1 and T2 (mcDESPOT) is an alternative to established multiecho T2 -based approaches for quantifying myelin water fraction, affording increased volumetric coverage and spatial resolution. A concern with mcDESPOT, however, is the large number of model parameters that must be estimated, which may lead to nonunique solutions and sensitivity to fitting constraints. Here we explore mcDESPOT performance under different experimental conditions to better understand the method's sensitivity and reliability.
To obtain parameter estimates, mcDESPOT uses a stochastic region contraction (SRC) approach to iteratively contract a predefined solution search-space around a global optimum. The sensitivity of mcDESPOT estimates to SRC boundary conditions, and tissue parameters, was examined using numerical phantoms and acquired in vivo human data.
The SRC approach is described and shown to return robust myelin water estimates in both numerical phantoms and in vivo data under a range of experimental conditions. However, care must be taken in choosing the initial SRC boundary conditions, ensuring they are broad enough to encompass the "true" solution.
Results suggest that under the range of conditions examined, mcDESPOT can provide stabile and precise values.
多组分驱动平衡单脉冲T1和T2观测法（mcDESPOT）是一种用于量化髓鞘水分数的方法，可替代已有的基于多回波T2的方法，能提供更大的体积覆盖范围和空间分辨率。然而，mcDESPOT存在一个问题，即必须估计大量的模型参数，这可能导致非唯一解以及对拟合约束的敏感性。在此，我们探讨mcDESPOT在不同实验条件下的性能，以更好地理解该方法的敏感性和可靠性。
为获得参数估计值，mcDESPOT使用随机区域收缩（SRC）方法，围绕全局最优值迭代收缩预定义的解搜索空间。使用数值体模和采集的人体活体数据，研究了mcDESPOT估计值对SRC边界条件和组织参数的敏感性。
对SRC方法进行了描述，并表明在一系列实验条件下，该方法在数值体模和活体数据中均能返回可靠的髓鞘水估计值。然而，在选择初始SRC边界条件时必须谨慎，确保其足够宽泛以涵盖“真实”解。
结果表明，在所研究的条件范围内，mcDESPOT能够提供稳定且精确的值。
