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 a...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.展开更多
文摘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.
