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Abstract #4343

A Microstructural Estimation Transformer with Sparse Coding for NODDI (METSCN)

Tianshu Zheng1, Yi-Cheng Hsu2, Yi Sun2, Yi Zhang1, Chuyang Ye3, and Dan Wu1
1Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China, Hangzhou, China, 2MR Collaboration, Siemens Healthineers Ltd., Shanghai, China, Shanghai, China, 3School of Information and Electronics, Beijing Institute of Technology, Beijing, China, Beijing, China


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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