@inproceedings{e9d0b624d7674731a20864dae6be587d,
title = "Multifold Acceleration of Diffusion MRI via Deep Learning Reconstruction from Slice-Undersampled Data",
abstract = "Diffusion MRI (dMRI), while powerful for characterization of tissue microstructure, suffers from long acquisition time. In this paper, we present a method for effective diffusion MRI reconstruction from slice-undersampled data. Instead of full diffusion-weighted (DW) image volumes, only a subsample of equally-spaced slices need to be acquired. We show that complementary information from DW volumes corresponding to different diffusion wavevectors can be harnessed using graph convolutional neural networks for reconstruction of the full DW volumes. The experimental results indicate a high acceleration factor of up{\^A} to 5 can be achieved with minimal information loss.",
keywords = "Accelerated acquisition, Adversarial learning, Diffusion MRI, Graph CNN, Super resolution",
author = "Yoonmi Hong and Geng Chen and Yap, {Pew Thian} and Dinggang Shen",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 26th International Conference on Information Processing in Medical Imaging, IPMI 2019 ; Conference date: 02-06-2019 Through 07-06-2019",
year = "2019",
doi = "10.1007/978-3-030-20351-1_41",
language = "英语",
isbn = "9783030203504",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "530--541",
editor = "Chung, {Albert C.S.} and Gee, {James C.} and Yushkevich, {Paul A.} and Siqi Bao",
booktitle = "Information Processing in Medical Imaging - 26th International Conference, IPMI 2019, Proceedings",
}