Echo time‐range effects on gradient‐echo based myelin water fraction mapping at 3T

Abstract

Purpose To investigate the effect of the range of TE on multi‐echo gradient echo–based myelin water fraction (MWF) mapping at 3 T. Methods Myelin water fraction estimation was performed using 3 widely used models: magnitude multipool, magnitude 3‐pool, and complex 3‐pool model. Simulations were conducted using a hollow cylinder model with susceptibility anisotropy. The TE range was varied, and its effect on MWF estimation was observed. The same analysis was also performed with in vivo data from 4 healthy volunteers. Results Different MWFs were obtained depending on the range of TE data acquired. In the simulations, the results of both magnitude models had a bias with respect to the true values: a maximum bias of up to 20% for the multipool model, and a maximum bias of up to 8% for the 3‐pool model. This bias was reduced using the complex 3‐pool model (maximum bias was under 2%). These effects were also found in the in vivo data. The MWF values using the complex model was most stable with respect to the TE range. Conclusion The results of the simulation and in vivo data suggest that MWF values fitted using magnitude models are more influenced by the TE range collected for fitting than using the complex model. The complex model is helpful in mitigating bias due to dependencies over the TE range selection.

Blind Source Separation for Myelin Water Fraction Mapping Using Multi-Echo Gradient Echo Imaging

“…In this approach, the initial values and bounds of the fitting parameters need to be set to avoid getting trapped in local minima (e.g. see [21]).…”

Artificial neural network for multi‐echo gradient echo–based myelin water fraction estimation

“…Hence, the restricted number of water pools may hamper the use of ANNs by not reflecting the detailed aspects of practical changes occurring in tissues 9,37,38 . However, in previous studies, the three‐pool exponential model showed improved performance on MWF estimation using mGRE acquisitions 8,26,39 . Moreover, because of the increased sensitivity to physiological noise 11 and field inhomogeneities 13 in mGRE acquisitions, multicompartment model application with separable amplitudes and frequency offsets for an unknown number of water pools might be challenging.…”

“…9,37,38 However, in previous studies, the three-pool exponential model showed improved performance on MWF estimation using mGRE acquisitions. 8,26,39 Moreover, because of the increased sensitivity to physiological noise 11 and field inhomogeneities 13 in mGRE acquisitions, multicompartment model application with separable T * 2 amplitudes and frequency offsets for an unknown number of water pools might be challenging. Thus, only the three-pool exponential model was considered in this study, although different signal models could be adopted.…”

“…We acquired mGRE data for various scan parameters 26 . Particularly, 1 healthy subject was scanned for different averages to evaluate noise sensitivity.…”

“…25 We acquired mGRE data for various scan parameters. 26 Particularly, 1 healthy subject was scanned for different averages to evaluate noise sensitivity. To investigate the feasibility of high-resolution MWI, data with various resolutions were acquired for another subject with varying resolution from 2 mm to 1 mm.…”