Piredda Gian Franco, Hilbert Tom, Ravano Veronica, Canales-Rodríguez Erick Jorge, Pizzolato Marco, Meuli Reto, Thiran Jean-Philippe, Richiardi Jonas, Kober Tobias
Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland.
Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
NMR Biomed. 2022 Jul;35(7):e4668. doi: 10.1002/nbm.4668. Epub 2021 Dec 22.
Long acquisition times preclude the application of multiecho spin echo (MESE) sequences for myelin water fraction (MWF) mapping in daily clinical practice. In search of alternative methods, previous studies of interest explored the biophysical modeling of MWF from measurements of different tissue properties that can be obtained in scan times shorter than those required for the MESE. In this work, a novel data-driven estimation of MWF maps from fast relaxometry measurements is proposed and investigated. T and T relaxometry maps were acquired in a cohort of 20 healthy subjects along with a conventional MESE sequence. Whole-brain quantitative mapping was achieved with a fast protocol in 6 min 24 s. Reference MWF maps were derived from the MESE sequence (TA = 11 min 17 s) and their data-driven estimation from relaxometry measurements was investigated using three different modeling strategies: two general linear models (GLMs) with linear and quadratic regressors, respectively; a random forest regression model; and two deep neural network architectures, a U-Net and a conditional generative adversarial network (cGAN). Models were validated using a 10-fold crossvalidation. The resulting maps were visually and quantitatively compared by computing the root mean squared error (RMSE) between the estimated and reference MWF maps, the intraclass correlation coefficients (ICCs) between corresponding MWF values in different brain regions, and by performing Bland-Altman analysis. Qualitatively, the estimated maps appear to generally provide a similar, yet more blurred MWF contrast in comparison with the reference, with the cGAN model best capturing MWF variabilities in small structures. By estimating the average adjusted coefficient of determination of the GLM with quadratic regressors, we showed that 87% of the variability in the MWF values can be explained by relaxation times alone. Further quantitative analysis showed an average RMSE smaller than 0.1% for all methods. The ICC was greater than 0.81 for all methods, and the bias smaller than 2.19%. It was concluded that this work confirms the notion that relaxometry parameters contain a large part of the information on myelin water and that MWF maps can be generated from T /T data with minimal error. Among the investigated modeling approaches, the cGAN provided maps with the best trade-off between accuracy and blurriness. Fast relaxometry, like the 6 min 24 s whole-brain protocol used in this work in conjunction with machine learning, may thus have the potential to replace time-consuming MESE acquisitions.
较长的采集时间使得多回波自旋回波（MESE）序列无法在日常临床实践中用于髓磷脂水分数（MWF）成像。为了寻找替代方法，之前的相关研究探索了通过测量不同组织特性对MWF进行生物物理建模，这些特性可在比MESE所需时间更短的扫描时间内获得。在这项工作中，我们提出并研究了一种基于快速弛豫测量的MWF图的新型数据驱动估计方法。对20名健康受试者组成的队列采集了T1和T2弛豫测量图以及传统的MESE序列。采用快速协议在6分24秒内完成了全脑定量成像。参考MWF图由MESE序列（采集时间TA = 11分17秒）生成，并使用三种不同的建模策略研究了基于弛豫测量数据的数据驱动估计：两种分别带有线性和二次回归器的一般线性模型（GLM）；一个随机森林回归模型；以及两种深度神经网络架构，即U-Net和条件生成对抗网络（cGAN）。使用10折交叉验证对模型进行验证。通过计算估计的MWF图与参考MWF图之间的均方根误差（RMSE）、不同脑区对应MWF值之间的组内相关系数（ICC）以及进行Bland-Altman分析，对生成的图进行视觉和定量比较。定性地说，与参考图相比，估计的图通常提供了相似但更模糊的MWF对比度，其中cGAN模型在捕捉小结构中的MWF变异性方面表现最佳。通过估计带有二次回归器的GLM的平均调整决定系数，我们表明MWF值中87%的变异性仅由弛豫时间就能解释。进一步的定量分析表明，所有方法的平均RMSE均小于0.1%。所有方法的ICC均大于0.81，偏差小于2.19%。研究得出结论，这项工作证实了弛豫测量参数包含了关于髓磷脂水的大部分信息这一观点，并且可以从T1/T2数据生成MWF图，误差极小。在所研究的建模方法中，cGAN生成的图在准确性和模糊度之间实现了最佳平衡。因此，像本工作中使用的6分24秒全脑协议这样的快速弛豫测量结合机器学习，可能有潜力取代耗时的MESE采集。
