Fast reconstruction of highly undersampled MR images using one and two dimensional principal component analysis.

Abstract:

:Recent compressed sensing techniques allow signal acquisition with less sampling than required by the Nyquist-Shannon theorem which reduces the data acquisition time in magnetic resonance imaging (MRI). However, prior knowledge becomes essential to reconstruct detailed features when the sampling rate is exceedingly low. In this work, one compressed sensing scheme developed in wireless sensing networks was adapted for the purpose of reconstructing magnetic resonance images by using one-dimensional principal component analysis (1D-PCA). Moreover, another related reconstruction method was proposed based on two-dimensional principal component analysis (2D-PCA). When comparing with one wavelet compressed sensing method, we demonstrate that these techniques are feasible and efficient at high undersampling rates.

journal_name

Magn Reson Imaging

authors

Zong F,d'Eurydice MN,Galvosas P

doi

10.1016/j.mri.2015.10.009

subject

Has Abstract

pub_date

2016-02-01 00:00:00

pages

227-38

issue

2

eissn

0730-725X

issn

1873-5894

pii

S0730-725X(15)00246-5

journal_volume

34

pub_type

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