Parallel MRI reconstruction using variance partitioning regularization.

Abstract:

:Multiple receivers can be utilized to enhance the spatiotemporal resolution of MRI by employing the parallel imaging technique. Previously, we have reported the L-curve Tikhonov regularization technique to mitigate noise amplification resulting from the geometrical correlations between channels in a coil array. Nevertheless, one major disadvantage of regularized image reconstruction is lengthy computational time in regularization parameter estimation. At a fixed noise level, L-curve regularization parameter estimation was also found not to be robust across repetitive measurements, particularly for low signal-to-noise ratio (SNR) acquisitions. Here we report a computationally efficient and robust method to estimate the regularization parameter by partitioning the variance of the noise-whitened encoding matrix based on the estimated SNR of the aliased pixel set in parallel MRI data. The proposed Variance Partitioning Regularization (VPR) method can improve computational efficiency by 2-5-fold, depending on image matrix sizes and acceleration rates. Our anatomical and functional MRI results show that the VPR method can be applied to both static and dynamic MRI experiments to suppress noise amplification in parallel MRI reconstructions for improved image quality.

journal_name

Magn Reson Med

authors

Lin FH,Wang FN,Ahlfors SP,Hämäläinen MS,Belliveau JW

doi

10.1002/mrm.21356

subject

Has Abstract

pub_date

2007-10-01 00:00:00

pages

735-44

issue

4

eissn

0740-3194

issn

1522-2594

journal_volume

58

pub_type

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