TY - GEN
T1 - Towards Accurate Microstructure Estimation via 3D Hybrid Graph Transformer
AU - Yang, Junqing
AU - Jiang, Haotian
AU - Tassew, Tewodros
AU - Sun, Peng
AU - Ma, Jiquan
AU - Xia, Yong
AU - Yap, Pew Thian
AU - Chen, Geng
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
PY - 2023
Y1 - 2023
N2 - Deep learning has drawn increasing attention in microstructure estimation with undersampled diffusion MRI (dMRI) data. A representative method is the hybrid graph transformer (HGT), which achieves promising performance by integrating q-space graph learning and x-space transformer learning into a unified framework. However, this method overlooks the 3D spatial information as it relies on training with 2D slices. To address this limitation, we propose 3D hybrid graph transformer (3D-HGT), an advanced microstructure estimation model capable of making full use of 3D spatial information and angular information. To tackle the large computation burden associated with 3D x-space learning, we propose an efficient q-space learning model based on simplified graph neural networks. Furthermore, we propose a 3D x-space learning module based on the transformer. Extensive experiments on data from the human connectome project show that our 3D-HGT outperforms state-of-the-art methods, including HGT, in both quantitative and qualitative evaluations.
AB - Deep learning has drawn increasing attention in microstructure estimation with undersampled diffusion MRI (dMRI) data. A representative method is the hybrid graph transformer (HGT), which achieves promising performance by integrating q-space graph learning and x-space transformer learning into a unified framework. However, this method overlooks the 3D spatial information as it relies on training with 2D slices. To address this limitation, we propose 3D hybrid graph transformer (3D-HGT), an advanced microstructure estimation model capable of making full use of 3D spatial information and angular information. To tackle the large computation burden associated with 3D x-space learning, we propose an efficient q-space learning model based on simplified graph neural networks. Furthermore, we propose a 3D x-space learning module based on the transformer. Extensive experiments on data from the human connectome project show that our 3D-HGT outperforms state-of-the-art methods, including HGT, in both quantitative and qualitative evaluations.
KW - 3D Spatial Domain
KW - Graph Neural Network
KW - Microstructure Imaging
KW - Transformer
UR - http://www.scopus.com/inward/record.url?scp=85174696872&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-43993-3_3
DO - 10.1007/978-3-031-43993-3_3
M3 - 会议稿件
AN - SCOPUS:85174696872
SN - 9783031439926
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 25
EP - 34
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings
A2 - Greenspan, Hayit
A2 - Greenspan, Hayit
A2 - Madabhushi, Anant
A2 - Mousavi, Parvin
A2 - Salcudean, Septimiu
A2 - Duncan, James
A2 - Syeda-Mahmood, Tanveer
A2 - Taylor, Russell
PB - Springer Science and Business Media Deutschland GmbH
T2 - 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023
Y2 - 8 October 2023 through 12 October 2023
ER -