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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.


Three transaxial slices of the physical Hoffmanphantom. In (a), FBP-reconstructed images without any noise reduction. In (b),radial sinogram filtering: FBP images for Gaussian filtering (top row) and forL-filtering (bottom row) at the matched resolutions (see Figures 4 and 5). In(c), angular stackgram filtering: FBP images for Gaussian filtering (top row)and for L-filtering (bottom row) at the matched resolutions (Figures 4 and 5).The noise variation seems to be higher after angular stackgram filtering (c) incomparison with radial sinogram filtering (b). All images share a commongrayscale.
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fig10: Three transaxial slices of the physical Hoffmanphantom. In (a), FBP-reconstructed images without any noise reduction. In (b),radial sinogram filtering: FBP images for Gaussian filtering (top row) and forL-filtering (bottom row) at the matched resolutions (see Figures 4 and 5). In(c), angular stackgram filtering: FBP images for Gaussian filtering (top row)and for L-filtering (bottom row) at the matched resolutions (Figures 4 and 5).The noise variation seems to be higher after angular stackgram filtering (c) incomparison with radial sinogram filtering (b). All images share a commongrayscale.

Mentions: Figure 10(a) shows FBP images of the Hoffman phantom(FBP with ramp filter). In Figures 10(b) and 10(c) (top row), FBP images of theHoffman data are shown for radial sinogram and angular stackgram filtering withthe matched Gaussian filters. These FBP images are congruent with the showncurves (Figures 4 and 6); stackgram filtering tends to leave more noisevariation in the images (or this can be regarded as a powdery noise structure).


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)

Three transaxial slices of the physical Hoffmanphantom. In (a), FBP-reconstructed images without any noise reduction. In (b),radial sinogram filtering: FBP images for Gaussian filtering (top row) and forL-filtering (bottom row) at the matched resolutions (see Figures 4 and 5). In(c), angular stackgram filtering: FBP images for Gaussian filtering (top row)and for L-filtering (bottom row) at the matched resolutions (Figures 4 and 5).The noise variation seems to be higher after angular stackgram filtering (c) incomparison with radial sinogram filtering (b). All images share a commongrayscale.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2266980&req=5

fig10: Three transaxial slices of the physical Hoffmanphantom. In (a), FBP-reconstructed images without any noise reduction. In (b),radial sinogram filtering: FBP images for Gaussian filtering (top row) and forL-filtering (bottom row) at the matched resolutions (see Figures 4 and 5). In(c), angular stackgram filtering: FBP images for Gaussian filtering (top row)and for L-filtering (bottom row) at the matched resolutions (Figures 4 and 5).The noise variation seems to be higher after angular stackgram filtering (c) incomparison with radial sinogram filtering (b). All images share a commongrayscale.
Mentions: Figure 10(a) shows FBP images of the Hoffman phantom(FBP with ramp filter). In Figures 10(b) and 10(c) (top row), FBP images of theHoffman data are shown for radial sinogram and angular stackgram filtering withthe matched Gaussian filters. These FBP images are congruent with the showncurves (Figures 4 and 6); stackgram filtering tends to leave more noisevariation in the images (or this can be regarded as a powdery noise structure).

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.