Filtered deconvolution of a simulated and an in vivo phase model of the human brain

Grabner, Gunther, Trattnig, Siegfried and Barth, Markus (2010) Filtered deconvolution of a simulated and an in vivo phase model of the human brain. Journal of Magnetic Resonance Imaging, 32 2: 289-297. doi:10.1002/jmri.22246

Author Grabner, Gunther
Trattnig, Siegfried
Barth, Markus
Title Filtered deconvolution of a simulated and an in vivo phase model of the human brain
Journal name Journal of Magnetic Resonance Imaging   Check publisher's open access policy
ISSN 1053-1807
Publication date 2010-01-01
Year available 2010
Sub-type Article (original research)
DOI 10.1002/jmri.22246
Volume 32
Issue 2
Start page 289
End page 297
Total pages 9
Place of publication Hoboken, NJ United States
Publisher John Wiley and Sons, Inc.
Language eng
Subject 2741 Radiology Nuclear Medicine and imaging
Abstract Purpose: To remove spatial patterns in gradient echo phase images which are caused by susceptibility differences between different tissue types using filtered deconvolution and to evaluate deconvolution effects. Materials and Methods: A realistic simulated susceptibility map of the human brain was built and used to evaluate the effects of filtered deconvolution. The simulated susceptibility map was convolved with a filter kernel representing a magnetic dipole resulting in a simulated phase map. The artificial phase map was superimposed with different noise levels and deconvolved using different deconvolution kernels. The resulting contrast-to-noise ratios between white and gray matter of the deconvolved data provide an estimate for an optimal deconvolution kernel for a given noise level. These results were used to deconvolve an in vivo phase model representing the average of 30 phase data sets and also individual phase data acquired at 7 Tesla. Results: The deconvolved phase model shows a better anatomical agreement with the corresponding magnitude than the original phase model (5% higher κ coefficient). Visual inspection of the deconvolved individual phase shows a more consistent delineation of blood vessels. Conclusion: Filtered deconvolution of SWI phase is possible when an appropriate filter kernel is used. This helps to improve region of interest definition as unrealistic phase patterns are removed.
Keyword Brain nuclei
Filtered deconvolution
ROI definition
Susceptibility-weighted imaging
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: Centre for Advanced Imaging Publications
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