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Experimental investigation of angular stackgram filtering for noise reduction of SPECT projection data: study with linear and nonlinear filters.

Happonen AP, Koskinen MO - Int J Biomed Imaging (2007)

Bottom Line: Our study is carried out by employing simple linear and nonlinear filters with ten different Gaussian kernels, in order to provide a comparable investigation.According to our results, angular stackgram filtering with the nonlinear filters provides the best resolution-noise tradeoff of the compared methods.Besides, stackgram filtering with these filters seems to preserve the resolution in an exceptional way.

View Article: PubMed Central - PubMed

Affiliation: Department of Clinical Physiology, Medical Imaging Center, Tampere University Hospital, P.O. Box 2000, 33521 Tampere, Finland.

ABSTRACT
We discuss data filtering prior to image reconstruction. For this kind of filtering, the radial direction of the sinogram is routinely employed. Recently, we have introduced an alternative approach to sinogram data processing, exploiting the angular information in a novel way. This new stackgram representation can be regarded as an intermediate form of the sinogram and image domains. In this experimental study, we compare the radial sinogram and angular stackgram filtering methods using physical SPECT phantoms. Our study is carried out by employing simple linear and nonlinear filters with ten different Gaussian kernels, in order to provide a comparable investigation. According to our results, angular stackgram filtering with the nonlinear filters provides the best resolution-noise tradeoff of the compared methods. Besides, stackgram filtering with these filters seems to preserve the resolution in an exceptional way. Visually, noise in the reconstructed images after stackgram filtering appears more "powdery" in comparison with radial sinogram filtering.

No MeSH data available.


Resolution-noise tradeoff curves for the compared methods with the L-filters.The points of the curves represent the kernel widths of the 10 nonlinearfilters. The lines simply connect the points. The kernel width gets wider(i.e., the filtering strength increases) from top-right to bottom. Angularstackgram filtering provides a better tradeoff than radial sinogram filteringfor all the filter kernels. The chosen or matched resolution level is alsoshown.
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fig5: Resolution-noise tradeoff curves for the compared methods with the L-filters.The points of the curves represent the kernel widths of the 10 nonlinearfilters. The lines simply connect the points. The kernel width gets wider(i.e., the filtering strength increases) from top-right to bottom. Angularstackgram filtering provides a better tradeoff than radial sinogram filteringfor all the filter kernels. The chosen or matched resolution level is alsoshown.

Mentions: Resolution-noise tradeoff curves of the comparedmethods are shown in Figures 4 and 5 for the Gaussian filters and theL-filters, respectively. With the linear Gaussian filters, radial sinogramfiltering provides a better tradeoff than angular stackgram filtering atsuitable resolution levels (see Figure 4). Overall, taking into considerationboth filter types (Gaussian and L-filters), stackgram filtering with theL-filters provides the best tradeoff in terms of noise reduction at theappropriate resolution, although the differences in the tradeoffs are not sosignificant (compare Figure 4 to Figure 5). Note that in Figure 5 the measureddata values for stackgram filtering below a CR of 0.5 are somewhat biased, sincethe curve suggests that noise or CoV increases. In this case, however, thefilters below this CR value are insignificant in our investigation; becausesuch filters are impractical for noise reduction (i.e., they provide a toonarrow contrast).


Experimental investigation of angular stackgram filtering for noise reduction of SPECT projection data: study with linear and nonlinear filters.

Happonen AP, Koskinen MO - Int J Biomed Imaging (2007)

Resolution-noise tradeoff curves for the compared methods with the L-filters.The points of the curves represent the kernel widths of the 10 nonlinearfilters. The lines simply connect the points. The kernel width gets wider(i.e., the filtering strength increases) from top-right to bottom. Angularstackgram filtering provides a better tradeoff than radial sinogram filteringfor all the filter kernels. The chosen or matched resolution level is alsoshown.
© Copyright Policy - open-access
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2266980&req=5

fig5: Resolution-noise tradeoff curves for the compared methods with the L-filters.The points of the curves represent the kernel widths of the 10 nonlinearfilters. The lines simply connect the points. The kernel width gets wider(i.e., the filtering strength increases) from top-right to bottom. Angularstackgram filtering provides a better tradeoff than radial sinogram filteringfor all the filter kernels. The chosen or matched resolution level is alsoshown.
Mentions: Resolution-noise tradeoff curves of the comparedmethods are shown in Figures 4 and 5 for the Gaussian filters and theL-filters, respectively. With the linear Gaussian filters, radial sinogramfiltering provides a better tradeoff than angular stackgram filtering atsuitable resolution levels (see Figure 4). Overall, taking into considerationboth filter types (Gaussian and L-filters), stackgram filtering with theL-filters provides the best tradeoff in terms of noise reduction at theappropriate resolution, although the differences in the tradeoffs are not sosignificant (compare Figure 4 to Figure 5). Note that in Figure 5 the measureddata values for stackgram filtering below a CR of 0.5 are somewhat biased, sincethe curve suggests that noise or CoV increases. In this case, however, thefilters below this CR value are insignificant in our investigation; becausesuch filters are impractical for noise reduction (i.e., they provide a toonarrow contrast).

Bottom Line: Our study is carried out by employing simple linear and nonlinear filters with ten different Gaussian kernels, in order to provide a comparable investigation.According to our results, angular stackgram filtering with the nonlinear filters provides the best resolution-noise tradeoff of the compared methods.Besides, stackgram filtering with these filters seems to preserve the resolution in an exceptional way.

View Article: PubMed Central - PubMed

Affiliation: Department of Clinical Physiology, Medical Imaging Center, Tampere University Hospital, P.O. Box 2000, 33521 Tampere, Finland.

ABSTRACT
We discuss data filtering prior to image reconstruction. For this kind of filtering, the radial direction of the sinogram is routinely employed. Recently, we have introduced an alternative approach to sinogram data processing, exploiting the angular information in a novel way. This new stackgram representation can be regarded as an intermediate form of the sinogram and image domains. In this experimental study, we compare the radial sinogram and angular stackgram filtering methods using physical SPECT phantoms. Our study is carried out by employing simple linear and nonlinear filters with ten different Gaussian kernels, in order to provide a comparable investigation. According to our results, angular stackgram filtering with the nonlinear filters provides the best resolution-noise tradeoff of the compared methods. Besides, stackgram filtering with these filters seems to preserve the resolution in an exceptional way. Visually, noise in the reconstructed images after stackgram filtering appears more "powdery" in comparison with radial sinogram filtering.

No MeSH data available.