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Erschienen in: Medical & Biological Engineering & Computing 8/2017

08.11.2016 | Original Article

Considerations in applying compressed sensing to in vivo phosphorus MR spectroscopic imaging of human brain at 3T

verfasst von: Gokce Hale Hatay, Muhammed Yildirim, Esin Ozturk-Isik

Erschienen in: Medical & Biological Engineering & Computing | Ausgabe 8/2017

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Abstract

The purpose of this study was to apply compressed sensing method for accelerated phosphorus MR spectroscopic imaging (31P-MRSI) of human brain in vivo at 3T. Fast 31P-MRSI data of five volunteers were acquired on a 3T clinical MR scanner using pulse-acquire sequence with a pseudorandom undersampling pattern for a data reduction factor of 5.33 and were reconstructed using compressed sensing. Additionally, simulated 31P-MRSI human brain tumor datasets were created to analyze the effects of k-space sampling pattern, data matrix size, regularization parameters of the reconstruction, and noise on the compressed sensing accelerated 31P-MRSI data. The 31P metabolite peak ratios of the full and compressed sensing accelerated datasets of healthy volunteers in vivo were similar according to the results of a Bland–Altman test. The estimated effective spatial resolution increased with reduction factor and sampling more at the k-space center. A lower regularization parameter for both total variation and L1-norm penalties resulted in a better compressed sensing reconstruction of 31P-MRSI. Although the root-mean-square error increased with noise levels, the compressed sensing reconstruction was robust for up to a reduction factor of 10 for the simulated data that had sharply defined tumor borders. As a result, compressed sensing was successfully applied to accelerate 31P-MRSI of human brain in vivo at 3T.

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Metadaten
Titel
Considerations in applying compressed sensing to in vivo phosphorus MR spectroscopic imaging of human brain at 3T
verfasst von
Gokce Hale Hatay
Muhammed Yildirim
Esin Ozturk-Isik
Publikationsdatum
08.11.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
Medical & Biological Engineering & Computing / Ausgabe 8/2017
Print ISSN: 0140-0118
Elektronische ISSN: 1741-0444
DOI
https://doi.org/10.1007/s11517-016-1591-9

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