Myelin water fraction (MWF) mapping can substantially improve our understanding of several demyelinating diseases. While MWF maps can be obtained from multi-exponential fitting of multi-echo imaging data, current solutions are often very sensitive to noise and modeling errors. This work addresses this problem using a new model-based method. This method has two key novel features: a) an improved signal model capable of compensating practical signal errors, and b) incorporation of parameter distributions and low-rank signal structures. Both simulation and experimental results show that the proposed method significantly outperforms the conventional methods currently used for MWF estimation.
This abstract and the presentation materials are available to members only; a login is required.