The Open-Access Journal for the Basic Principles of Diffusion Theory, Experiment and Application Reconstructing undersampled MR Images by utilizing principal-component-analysis-based pattern recognition Fangrong Zong 1 , Marcel Nogueira d’Eurydice 1 , Petrik Galvosas 1 1 MacDiarmid Institute for Advanced Materials and Nanotechnology,Victoria University of Wellington, Wellington, New Zealand Corresponding author: Petrik Galvosas, MacDiarmid Institute for Advanced Materials and Nan- otechnology, School of Chemical and Physical Sciences, Victoria University of Wellington, PO Box 600, Wellington 6140, New Zealand. E-mail: Petrik.Galvosas@vuw.ac.nz. Abstract 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. Keywords: MRI; Fast imaging; Principal components analysis; Recognition; Compressed sensing; 1 Introduction Acquisition of MRI in clinical applications may be time-consuming and may lead to reduced patient throughputs and increased image artefacts due to the patient moving during imaging. Reducing imaging time is financially beneficial to a total cost and can facilitate more studies as well. One approach of speeding up the acquisition of MRI is to undersample k-space data. Aliasing artefacts due to the violation of Nyquist-Shannon rule [1, 2] in the reconstructed magnetic resonance (MR) images can be reduced by a randomly undersampling format [3, 4], which is part of the Compressed Sensing (CS) framework. CS techniques have been extensively used in undersampled MRI reconstruction since 2006 [5–8]. The premise of applying a CS framework is that the MR images are sparse in a certain orthogonal transformation domain (aka basis) [5, 9]. However, when the object is largely undersampled, the missing features may not be satisfyingly reconstructed. In this situation, prior knowledge of similar images can provide constructive information to recover those features, such as shapes and relative contrast to the surrounding tissues. Principal component analysis (PCA) is a useful statistical technique that extracting the principal components from a set of objects (similar images, in our case) [10, 11]. The number of principal components is less than or equal to the number of images in the database, thus the combination of © 2014, Fangrong Zong diffusion-fundamentals.org 22 (2014) 14, pp 1-5 1