TY - GEN
T1 - An Adaptive Network with Extragradient for Diffusion MRI-Based Microstructure Estimation
AU - Zheng, Tianshu
AU - Zheng, Weihao
AU - Sun, Yi
AU - Zhang, Yi
AU - Ye, Chuyang
AU - Wu, Dan
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Diffusion MRI (dMRI) is a powerful tool for probing tissue microstructural properties. However, advanced dMRI models are commonly nonlinear and complex, which requires densely sampled q-space and is prone to estimation errors. This problem can be resolved using deep learning techniques, especially optimization-based networks. In previous optimization-based methods, the number of iterative blocks was selected empirically. Furthermore, previous network structures were based on the iterative shrinkage-thresholding algorithm (ISTA), which could result in instability during sparse reconstruction. In this work, we proposed an adaptive network with extragradient for diffusion MRI-based microstructure estimation (AEME) by introducing an additional projection of the extragradient, such that the convergence of the network can be guaranteed. Meanwhile, with the adaptive iterative selection module, the sparse representation process can be modeled flexibly according to specific dMRI models. The network was evaluated on the neurite orientation dispersion and density imaging (NODDI) model on a public 3T and a private 7T dataset. AEME showed superior improved accuracy and generalizability compared to other state-of-the-art microstructural estimation algorithms.
AB - Diffusion MRI (dMRI) is a powerful tool for probing tissue microstructural properties. However, advanced dMRI models are commonly nonlinear and complex, which requires densely sampled q-space and is prone to estimation errors. This problem can be resolved using deep learning techniques, especially optimization-based networks. In previous optimization-based methods, the number of iterative blocks was selected empirically. Furthermore, previous network structures were based on the iterative shrinkage-thresholding algorithm (ISTA), which could result in instability during sparse reconstruction. In this work, we proposed an adaptive network with extragradient for diffusion MRI-based microstructure estimation (AEME) by introducing an additional projection of the extragradient, such that the convergence of the network can be guaranteed. Meanwhile, with the adaptive iterative selection module, the sparse representation process can be modeled flexibly according to specific dMRI models. The network was evaluated on the neurite orientation dispersion and density imaging (NODDI) model on a public 3T and a private 7T dataset. AEME showed superior improved accuracy and generalizability compared to other state-of-the-art microstructural estimation algorithms.
KW - Adaptive mechanism
KW - Diffusion MRI
KW - Extragradient
KW - Microstructure estimation
KW - q-Space acceleration
UR - http://www.scopus.com/inward/record.url?scp=85138812130&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-16431-6_15
DO - 10.1007/978-3-031-16431-6_15
M3 - Conference contribution
AN - SCOPUS:85138812130
SN - 9783031164309
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 153
EP - 162
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings
A2 - Wang, Linwei
A2 - Dou, Qi
A2 - Fletcher, P. Thomas
A2 - Speidel, Stefanie
A2 - Li, Shuo
PB - Springer Science and Business Media Deutschland GmbH
T2 - 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022
Y2 - 18 September 2022 through 22 September 2022
ER -