A statistical examination of SENSE image reconstruction via an isomorphism representation

Document Type

Article

Language

eng

Format of Original

21 p.

Publication Date

11-2011

Publisher

Elsevier

Source Publication

Magnetic Resonance Imaging

Source ISSN

0730-725X

Original Item ID

doi: 10.1016/j.mri.2011.07.016

Abstract

In magnetic resonance imaging, the parallel acquisition of subsampled spatial frequencies from an array of multiple receiver coils has become a common means of reducing data acquisition time. SENSitivity Encoding (SENSE) is a popular parallel image reconstruction model that uses a complex-valued least squares estimation process to unfold aliased images. In this article, the linear mathematical framework derived in Rowe et al. [J Neurosci Meth 159 (2007) 361–369] is built upon to perform image reconstruction with subsampled data acquired from multiple receiver coils, where the SENSE model is represented as a real-valued isomorphism. A statistical analysis is performed of the various image reconstruction operators utilized in the SENSE model, with an emphasis placed on the effects of each operator on voxel means, variances and correlations. It is shown that, despite the attractiveness of models that unfold the aliased images from subsampled data, there is an artificial correlation induced between reconstructed voxels from the different folds of aliased images. As such, the mathematical framework outlined in this manuscript could be further developed to provide a means of accounting for this unavoidable correlation induced by image reconstruction operators.

Comments

Magnetic Resonance Imaging, Vol.29, No. 9 (November 2011): 1267-1287. DOI.

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