Reconstructing undersampled MR Images by utilizing principal-component-analysis-based pattern recognition
نویسندگان
چکیده
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.
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تاریخ انتشار 2014