Chapter title |
q-Space Upsampling Using x-q Space Regularization
|
---|---|
Chapter number | 71 |
Book title |
Medical Image Computing and Computer Assisted Intervention − MICCAI 2017
|
Published in |
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, September 2017
|
DOI | 10.1007/978-3-319-66182-7_71 |
Pubmed ID | |
Book ISBNs |
978-3-31-966181-0, 978-3-31-966182-7
|
Authors |
Geng Chen, Bin Dong, Yong Zhang, Dinggang Shen, Pew-Thian Yap |
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). |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 9 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 2 | 22% |
Student > Bachelor | 1 | 11% |
Student > Doctoral Student | 1 | 11% |
Student > Master | 1 | 11% |
Student > Postgraduate | 1 | 11% |
Other | 0 | 0% |
Unknown | 3 | 33% |
Readers by discipline | Count | As % |
---|---|---|
Engineering | 2 | 22% |
Computer Science | 1 | 11% |
Agricultural and Biological Sciences | 1 | 11% |
Medicine and Dentistry | 1 | 11% |
Energy | 1 | 11% |
Other | 0 | 0% |
Unknown | 3 | 33% |