Journal article icon

Journal article

Recovering task fMRI signals from highly under-sampled data with low-rank and temporal subspace constraints

Abstract:

Recent developments in highly accelerated fMRI data acquisition have employed low-rank and/or sparsity constraints for image reconstruction, as an alternative to conventional, time-independent parallel imaging. When under-sampling factors are high or the signals of interest are low-variance, however, functional data recovery can be poor or incomplete. We introduce a method for improving reconstruction fidelity using external constraints, like an experimental design matrix, to partially orient...

Expand abstract
Publication status:
Published
Peer review status:
Peer reviewed
Version:
Publisher's version

Actions


Access Document


Files:
Publisher copy:
10.1016/j.neuroimage.2018.02.062

Authors


More by this author
Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Clinical Neurosciences
Graedel, NN More by this author
More by this author
Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Clinical Neurosciences
Royal Academy of Engineering More from this funder
More from this funder
Grant:
202788/Z/16/Z
203139/Z/16/Z
Publisher:
Elsevier Publisher's website
Journal:
NeuroImage Journal website
Volume:
174
Pages:
97–110
Publication date:
2018-03-29
Acceptance date:
2018-02-28
DOI:
EISSN:
1095-9572
ISSN:
1053-8119
Pubs id:
pubs:828509
URN:
uri:57f22759-9b25-46c2-8f28-d92e30d75e5b
UUID:
uuid:57f22759-9b25-46c2-8f28-d92e30d75e5b
Local pid:
pubs:828509
Language:
English

Terms of use


Metrics



If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP