Deconvolution algorithm based on automatic cutoff frequency selection for EPR imaging

The large line‐width associated with electron paramagnetic resonance imaging (EPRI) requires effective algorithms to deconvolve the true spatial profiles of spins from the measured projection data. The commonly used Fourier transform (FT) deconvolution algorithm is easy to implement but suffers from...

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Vydáno v:Magnetic resonance in medicine Ročník 50; číslo 2; s. 444 - 448
Hlavní autoři: Deng, Yuanmu, He, Guanglong, Kuppusamy, Periannan, Zweier, Jay L.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.08.2003
Williams & Wilkins
Témata:
Algorithms
Animals
Biological and medical sciences
cutoff frequency
deconvolution
Digestive System - anatomy & histology
Electron Spin Resonance Spectroscopy - methods
EPRI
Fourier transform
image reconstruction
Imaging, Three-Dimensional
Medical sciences
Mice
Phantoms, Imaging
cutoff frequency
deconvolution
Fourier transform
EPRI
image reconstruction
ISSN:0740-3194, 1522-2594
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Shrnutí:The large line‐width associated with electron paramagnetic resonance imaging (EPRI) requires effective algorithms to deconvolve the true spatial profiles of spins from the measured projection data. The commonly used Fourier transform (FT) deconvolution algorithm is easy to implement but suffers from the division‐by‐zero problem. As a result, a couple of parameters are used to control the deconvolution performance. However, this is inconvenient and the deconvolution results are subject to the experience of the operators. In the present work we examined FT deconvolution for EPRI, and proposed an automatic algorithm to determine the cutoff frequency by calculating the piecewise variance of the division result of the Fourier amplitude spectra. The deconvolution algorithm and the filtered back‐projection image reconstruction algorithm were implemented and validated using 3D phantom and in vivo imaging data. It was clearly observed that the image resolution improved after deconvolution with the proposed algorithm. Magn Reson Med 50:444–448, 2003. © 2003 Wiley‐Liss, Inc.
Bibliografie:istex:90FB006DB8A01DE2F0210FE472CA19D0435326CA
NIH - No. EB000306; No. EB00254
ArticleID:MRM10533
ark:/67375/WNG-4L3NZ663-S
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0740-3194
1522-2594
DOI:10.1002/mrm.10533