Super-resolution in magnetic resonance imaging : a review
For the last 15 years, super-resolution (SR) algorithms have successfully been applied to magnetic resonance imaging (MRI) data to increase the spatial resolution of scans after acquisition has been performed, thus facilitating the doctors' diagnosis. The variety of application and techniques h...
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| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
2013
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| Online Access: | https://hdl.handle.net/10356/97447 http://hdl.handle.net/10220/13153 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | For the last 15 years, super-resolution (SR) algorithms have successfully been applied to magnetic resonance imaging (MRI) data to increase the spatial resolution of scans after acquisition has been performed, thus facilitating the doctors' diagnosis. The variety of application and techniques has grown ever since, especially in the MRI modality, showing the interest of the community to such postacquisition processing. This article presents a review of the general principle of SR as well as how this principle has been adapted to MRI data. The main algorithms and the principal acquisition protocols are detailed for both static and moving subjects. The presented strategies are discussed and compared according to the data specificities. Later, different ways of measuring the resolution enhancement and quantify the benefit of SR are detailed. Finally, unexplored perspectives on the application of SR to MRI data are discussed. |
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