Data‐driven myelin water imaging based on T1 and T2 relaxometry. (22nd December 2021)

• Record Type: Journal Article
• Authors: Piredda, Gian Franco; Hilbert, Tom; Ravano, Veronica; Canales‐Rodríguez, Erick Jorge; Pizzolato, Marco; Meuli, Reto; Thiran, Jean‐Philippe; Richiardi, Jonas; Kober, Tobias
• Is Part Of: NMR in biomedicine. Volume 35:Number 7(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-12-22
• Subjects: data‐driven estimation -- machine learning -- myelin water imaging -- relaxometry; Nuclear magnetic resonance -- Periodicals; Magnetic Resonance Spectroscopy -- Periodicals; 574
• DOI: 10.1002/nbm.4668
• Languages: English
• ISSNs: 0952-3480

Abstract

Abstract : 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. T1 and T2 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 T1 /T2 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. Abstract : This work proposed a novel data‐driven estimation of brain myelin water fraction (MWF) maps. More specifically, the direct estimation of reference MWF maps from T1 and T2 relaxometry maps was explored. Among the different modeling strategies that were investigated, MWF maps estimated with deep learning methods gave the best agreement with the reference values. Results suggest that the fast whole‐brain relaxometry protocol (6 min 24 s) employed in conjunction with machine learning may potentially replace time‐consuming MESE acquisitions.