-
J Magn Reson Imaging · Mar 2010
Comparative StudySmoothing that does not blur: effects of the anisotropic approach for evaluating diffusion tensor imaging data in the clinic.
- Marta Moraschi, Gisela E Hagberg, Margherita Di Paola, Gianfranco Spalletta, Bruno Maraviglia, and Federico Giove.
- MARBILab, Museo storico della fisica e Centro di studi e ricerche Enrico Fermi, c/o Fondazione Santa Lucia, Roma, Italy.
- J Magn Reson Imaging. 2010 Mar 1; 31 (3): 690-7.
PurposeTo compare the effects of anisotropic and Gaussian smoothing on the outcomes of diffusion tensor imaging (DTI) voxel-based (VB) analyses in the clinic, in terms of signal-to-noise ratio (SNR) enhancement and directional information and boundary structures preservation.Materials And MethodsDTI data of 30 Alzheimer's disease (AD) patients and 30 matched control subjects were obtained at 3T. Fractional anisotropy (FA) maps with variable degrees and quality (Gaussian and anisotropic) of smoothing were created and compared with an unsmoothed dataset. The two smoothing approaches were evaluated in terms of SNR improvements, capability to separate differential effects between patients and controls by a standard VB analysis, and level of artifacts introduced by the preprocessing.ResultsGaussian smoothing regionally biased the FA values and introduced a high variability of results in clinical analysis, greatly dependent on the kernel size. On the contrary, anisotropic smoothing proved itself capable of enhancing the SNR of images and maintaining boundary structures, with only moderate dependence of results on smoothing parameters.ConclusionOur study suggests that anisotropic smoothing is more suitable in DTI studies; however, regardless of technique, a moderate level of smoothing seems to be preferable considering the artifacts introduced by this manipulation.
Notes
Knowledge, pearl, summary or comment to share?You can also include formatting, links, images and footnotes in your notes
- Simple formatting can be added to notes, such as
*italics*
,_underline_
or**bold**
. - Superscript can be denoted by
<sup>text</sup>
and subscript<sub>text</sub>
. - Numbered or bulleted lists can be created using either numbered lines
1. 2. 3.
, hyphens-
or asterisks*
. - Links can be included with:
[my link to pubmed](http://pubmed.com)
- Images can be included with:
![alt text](https://bestmedicaljournal.com/study_graph.jpg "Image Title Text")
- For footnotes use
[^1](This is a footnote.)
inline. - Or use an inline reference
[^1]
to refer to a longer footnote elseweher in the document[^1]: This is a long footnote.
.