Diffusion MRI (dMRI) models play an important role in characterizing tissue microstructures, commonly in the form of multi-compartmental biophysical models that are mathematically complex and highly non-linear. Fitting of these models with conventional optimization techniques is prone to estimation errors and requires dense sampling of q-space. Here we present a learning-based framework for estimating microstructural parameters in the NODDI model, termed Microstructure Estimation Transformer with Sparse Coding for NODDI (METSCN). We tested its performance with reduced q-space samples. Compared with the existing learning-based NODDI estimation algorithms, METSCN achieved the best accuracy, precision, and robustness.
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