Tissue Microstructure Estimation of SANDI Based on Deep Network  

作　　者：Bingnan Gao Zhiwen Liu 

机构地区：[1]School of Integrated Circuit and Electronics,Beijing Institute of Technology,Beijing 100081,China

出　　处：《Journal of Beijing Institute of Technology》2023年第5期600-608,共9页北京理工大学学报（英文版）

摘　　要：Diffusion magnetic resonance imaging(dMRI)is a noninvasive method to capture the anisotropic pattern of water displacement in the neuronal tissue.The soma and neurite density imaging(SANDI)model introduced soma size and density to biophysical model for the first time.In addition to neurite density,it can achieve their joint estimation non-invasively using dMRI.In the traditional method,parameters of the SANDI are estimated in a maximum likelihood frame-work,where the nonlinear model fitting is computationally intensive.Also,the present methods require a large number of diffusion gradients.Efficient and accurate algorithms for tissue microstructure estimation of SANDI is still a challenge currently.Consequently,we introduce deep learning method for tissue microstructure estimation of the SANDI model.The model comprises two functional components.The first component produces the sparse representation of diffusion sig-nals of input patches.The second component computes tissue microstructure from the sparse repre-sentation given by the first component.The deep network can produce not only tissue microstruc-ture estimates but also the uncertainty of the estimates with a reduced number of diffusion gradi-ents.Then,multiple deep networks are trained and their results are fused for the final prediction of tissue microstructure and uncertainty quantification.The deep network was evaluated on the MGH Connectome Diffusion Microstructure Dataset.Results indicate that our approach outperforms the traditional methods in terms of estimation accuracy.

关 键 词：diffusion magnetic resonance imaging(dMRI) tissue microstructure soma and neurite density imaging(SANDI) deep learning 

分 类 号：R318[医药卫生—生物医学工程] TP391.41[医药卫生—基础医学]