@inproceedings{32deb7d6ef31432689a468c1567035a2,
title = "q-space upsampling using x-q space regularization",
abstract = "Acquisition time in diffusion MRI increases with the number of diffusion-weighted images that need to be acquired. Particularly in clinical settings, scan time is limited and only a sparse coverage of the vast q-space is possible. In this paper, we show how non-local self-similar information in the x-q space of diffusion MRI data can be harnessed for q-space upsampling. More specifically, we establish the relationships between signal measurements in x-q space using a patch matching mechanism that caters to unstructured data. We then encode these relationships in a graph and use it to regularize an inverse problem associated with recovering a high q-space resolution dataset from its low-resolution counterpart. Experimental results indicate that the high-resolution datasets reconstructed using the proposed method exhibit greater quality, both quantitatively and qualitatively, than those obtained using conventional methods, such as interpolation using spherical radial basis functions (SRBFs).",
author = "Geng Chen and Bin Dong and Yong Zhang and Dinggang Shen and Yap, \{Pew Thian\}",
note = "Publisher Copyright: {\textcopyright} 2017, Springer International Publishing AG.; 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017 ; Conference date: 11-09-2017 Through 13-09-2017",
year = "2017",
doi = "10.1007/978-3-319-66182-7\_71",
language = "英语",
isbn = "9783319661810",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "620--628",
editor = "Maxime Descoteaux and Simon Duchesne and Alfred Franz and Pierre Jannin and Collins, \{D. Louis\} and Lena Maier-Hein",
booktitle = "Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings",
}