Compressed sensing technique is a recent framework for signal sampling and recovery. It allows signal acquisition with less sampling than required by Nyquist-Shannon theorem and reduces data acquisition time in MRI. When the sampling rate is low, prior knowledge is essential to reconstruct the missing features. In this paper, a different reconstruction method is proposed by using the principal
component analysis based on pattern recognition. The experiments demonstrate that this method can reduce aliasing artefacts and achieve a high peak signal-to-noise ratio compared to a compressed sensing reconstruction.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:13520 |
| Date | January 2014 |
| Creators | Zong, Fangrong, D’Eurydice, Marcel Nogueira, Galvosas, Petrik |
| Contributors | Victoria University of Wellington, Universität Leipzig |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | doc-type:article, info:eu-repo/semantics/article, doc-type:Text |
| Source | Diffusion fundamentals 22 (2014) 14, S. 1-5 |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | urn:nbn:de:bsz:15-qucosa-178662, qucosa:13482 |
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