C.J. Gorter center for high field MRI, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands.
Department of Neurology, Leiden University Medical Center, Leiden, the Netherlands.
Magn Reson Med. 2020 Nov;84(5):2656-2670. doi: 10.1002/mrm.28290. Epub 2020 Apr 19.
Multi-echo spin-echo (MSE) transverse relaxometry mapping using multi-component models is used to study disease activity in neuromuscular disease by assessing the T of the myocytic component (T ). Current extended phase graph algorithms are not optimized for fat fractions above 50% and the effects of inaccuracies in the T calibration remain unexplored. Hence, we aimed to improve the performance of extended phase graph fitting methods over a large range of fat fractions, by including the slice-selection flip angle profile, a through-plane chemical-shift displacement correction, and optimized calibration of T .
Simulation experiments were used to study the influence of the slice flip-angle profile with chemical-shift and T estimations. Next, in vivo data from four neuromuscular disease cohorts were studied for different T calibration methods and T estimations.
Excluding slice flip-angle profiles or chemical-shift displacement resulted in a bias in T up to 10 ms. Furthermore, a wrongly calibrated T caused a bias of up to 4 ms in T . For the in vivo data, one-component calibration led to a lower T compared with a two-component method, and T decreased with increasing fat fractions.
In vivo data showed a decline in T for increasing fat fractions, which has important implications for clinical studies, especially in multicenter settings. We recommend using an extended phase graph-based model for fitting T from MSE sequences with two-component T calibration. Moreover, we recommend including the slice flip-angle profile in the model with correction for through-plane chemical-shift displacements.
多回波自旋回波(MSE)横向弛豫率图通过使用多分量模型,用于通过评估肌细胞成分(T1)的 T 来研究神经肌肉疾病中的疾病活动。目前的扩展相位图算法不适用于脂肪分数高于 50%的情况,并且 T 校准的不准确性的影响仍未得到探索。因此,我们旨在通过包括切片选择翻转角分布、贯穿平面化学位移位移校正和优化 T1 校准,来提高扩展相位图拟合方法在较大脂肪分数范围内的性能。
模拟实验用于研究切片翻转角分布、化学位移和 T1 估计对 T1 的影响。接下来,对来自四个神经肌肉疾病队列的体内数据进行了不同 T1 校准方法和 T1 估计的研究。
不包括切片翻转角分布或化学位移位移会导致 T1 偏差高达 10 毫秒。此外,校准错误的 T1 会导致 T1 偏差高达 4 毫秒。对于体内数据,单分量校准导致 T1 低于两分量方法,并且 T1 随脂肪分数的增加而降低。
体内数据显示 T1 随脂肪分数的增加而降低,这对临床研究具有重要意义,特别是在多中心研究中。我们建议使用基于扩展相位图的模型,通过双分量 T1 校准拟合 MSE 序列中的 T1。此外,我们建议在模型中包括切片翻转角分布,并校正贯穿平面化学位移位移。