DeepDTI: High-fidelity six-direction diffusion tensor imaging using deep learning

• Published in: NeuroImage (Orlando, Fla.) Vol. 219; p. 117017
• Main Authors: Tian, Qiyuan, Bilgic, Berkin, Fan, Qiuyun, Liao, Congyu, Ngamsombat, Chanon, Hu, Yuxin, Witzel, Thomas, Setsompop, Kawin, Polimeni, Jonathan R., Huang, Susie Y.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.10.2020; Elsevier Limited; Elsevier
• Subjects: Brain - diagnostic imaging; Brain research; Connectome; Convolutional neural network; Data redundancy; Deep Learning; Denoising; Diffusion tensor imaging; Diffusion Tensor Imaging - methods; Diffusion tractography; Humans; Image Processing, Computer-Assisted - methods; Magnetic resonance imaging; Methods; Nerve Net - diagnostic imaging; Neural networks; Neural Networks, Computer; Neuroimaging; Residual learning; Substantia alba; Tract-specific analysis; Deep learning; Diffusion tractography; Tract-specific analysis; Convolutional neural network; Residual learning; Data redundancy; Diffusion tensor imaging; Denoising

Diffusion tensor magnetic resonance imaging (DTI) is unsurpassed in its ability to map tissue microstructure and structural connectivity in the living human brain. Nonetheless, the angular sampling requirement for DTI leads to long scan times and poses a critical barrier to performing high-quality D...