- AutorIn
- Fangrong Zong
- Marcel Nogueira D’Eurydice
- Petrik Galvosas
- Titel
- Reconstructing undersampled MR Images by utilizingprincipal-component-analysis-based pattern recognition
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:15-qucosa-179265
- Quellenangabe
- Diffusion fundamentals - 22
- Quellenangabe
- Diffusion fundamentals 22 (2014) 14, S. 1-5
- Erstveröffentlichung
- 2014
- Abstract (EN)
- 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.
- Freie Schlagwörter (DE)
- Diffusion, Transport
- Freie Schlagwörter (EN)
- diffusion, transport
- Klassifikation (DDC)
- 530
- Herausgeber (Institution)
- Victoria University of Wellington
- Universität Leipzig
- URN Qucosa
- urn:nbn:de:bsz:15-qucosa-179265
- Veröffentlichungsdatum Qucosa
- 16.09.2015
- Dokumenttyp
- Artikel
- Sprache des Dokumentes
- Englisch