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Image quality transfer and applications in diffusion MRI

Alexander, Daniel C.; Zikic, Darko; Ghosh, Aurobrata; Tanno, Ryutaro; Wottschel, Viktor; Zhang, Jiaying; Kaden, Enrico; Dyrby, Tim B.; Sotiropoulos, Stamatios N.; Zhang, Hui; Criminisi, Antonio

Authors

Daniel C. Alexander

Darko Zikic

Aurobrata Ghosh

Ryutaro Tanno

Viktor Wottschel

Jiaying Zhang

Enrico Kaden

Tim B. Dyrby

Stamatios N. Sotiropoulos

Hui Zhang

Antonio Criminisi



Abstract

This paper introduces a new computational imaging technique called image quality transfer (IQT). IQT uses machine learning to transfer the rich information available from one-off experimental medical imaging devices to the abundant but lower-quality data from routine acquisitions. The procedure uses matched pairs to learn mappings from low-quality to corresponding high-quality images. Once learned, these mappings then augment unseen low quality images, for example by enhancing image resolution or information content. Here, we demonstrate IQT using a simple patch-regression implementation and the uniquely rich diffusion MRI data set from the human connectome project (HCP). Results highlight potential benefits of IQT in both brain connectivity mapping and microstructure imaging. In brain connectivity mapping, IQT reveals, from standard data sets, thin connection pathways that tractography normally requires specialised data to reconstruct. In microstructure imaging, IQT shows potential in estimating, from standard “single-shell” data (one non-zero b-value), maps of microstructural parameters that normally require specialised multi-shell data. Further experiments show strong generalisability, highlighting IQT's benefits even when the training set does not directly represent the application domain. The concept extends naturally to many other imaging modalities and reconstruction problems.

Citation

Alexander, D. C., Zikic, D., Ghosh, A., Tanno, R., Wottschel, V., Zhang, J., …Criminisi, A. (2017). Image quality transfer and applications in diffusion MRI. NeuroImage, 152, https://doi.org/10.1016/j.neuroimage.2017.02.089

Journal Article Type Article
Acceptance Date Feb 28, 2017
Online Publication Date Mar 3, 2017
Publication Date May 15, 2017
Deposit Date Mar 7, 2017
Publicly Available Date Mar 7, 2017
Journal NeuroImage
Print ISSN 1053-8119
Electronic ISSN 1095-9572
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 152
DOI https://doi.org/10.1016/j.neuroimage.2017.02.089
Public URL http://eprints.nottingham.ac.uk/id/eprint/41123
Publisher URL http://www.sciencedirect.com/science/article/pii/S1053811917302008
Copyright Statement Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by/4.0

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by/4.0







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