Chapter title |
Motion-Robust Reconstruction Based on Simultaneous Multi-slice Registration for Diffusion-Weighted MRI of Moving Subjects
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Chapter number | 63 |
Book title |
Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2016
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Published in |
Lecture notes in computer science, October 2016
|
DOI | 10.1007/978-3-319-46726-9_63 |
Pubmed ID | |
Book ISBNs |
978-3-31-946725-2, 978-3-31-946726-9
|
Authors |
Bahram Marami, Benoit Scherrer, Onur Afacan, Simon K. Warfield, Ali Gholipour |
Editors |
Sebastien Ourselin, Leo Joskowicz, Mert R. Sabuncu, Gozde Unal, William Wells |
Abstract |
Simultaneous multi-slice (SMS) echo-planar imaging has had a huge impact on the acceleration and routine use of diffusion-weighted MRI (DWI) in neuroimaging studies in particular the human connectome project; but also holds the potential to facilitate DWI of moving subjects, as proposed by the new technique developed in this paper. We present a novel registration-based motion tracking technique that takes advantage of the multi-plane coverage of the anatomy by simultaneously acquired slices to enable robust reconstruction of neural microstructure from SMS DWI of moving subjects. Our technique constitutes three main components: 1) motion tracking and estimation using SMS registration, 2) detection and rejection of intra-slice motion, and 3) robust reconstruction. Quantitative results from 14 volunteer subject experiments and the analysis of motion-corrupted SMS DWI of 6 children indicate robust reconstruction in the presence of continuous motion and the potential to extend the use of SMS DWI in very challenging populations. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United States | 1 | 4% |
Unknown | 24 | 96% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 6 | 24% |
Researcher | 5 | 20% |
Student > Master | 3 | 12% |
Student > Doctoral Student | 2 | 8% |
Lecturer | 1 | 4% |
Other | 3 | 12% |
Unknown | 5 | 20% |
Readers by discipline | Count | As % |
---|---|---|
Engineering | 5 | 20% |
Medicine and Dentistry | 4 | 16% |
Computer Science | 4 | 16% |
Mathematics | 1 | 4% |
Neuroscience | 1 | 4% |
Other | 1 | 4% |
Unknown | 9 | 36% |