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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: We discuss data filtering prior to image reconstruction.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.

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.


Related in: MedlinePlus

Maximum-intensity-value versus FAHM for the compared methods with the nonlinearL-filters. The data points represent the different filter kernels. The filterwidth or length increases from top to bottom. A polynomial of the first degreewas fitted for the three radial filtering data points, whereas a line wasfitted for the quantified stackgram data. Most of the obtained values forradial filtering were omitted from the plot and from the polynomial fit, sincethe values did not fit the appropriate scale. The highest stackgram data valuewas excluded from the line fit. Angular stackgram filtering seems to preservethe resolution (or FAHM) almost perfectly, at the cost of decreasing maximumcount value.
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fig7: Maximum-intensity-value versus FAHM for the compared methods with the nonlinearL-filters. The data points represent the different filter kernels. The filterwidth or length increases from top to bottom. A polynomial of the first degreewas fitted for the three radial filtering data points, whereas a line wasfitted for the quantified stackgram data. Most of the obtained values forradial filtering were omitted from the plot and from the polynomial fit, sincethe values did not fit the appropriate scale. The highest stackgram data valuewas excluded from the line fit. Angular stackgram filtering seems to preservethe resolution (or FAHM) almost perfectly, at the cost of decreasing maximumcount value.

Mentions: FAHM versus maximum-intensity-value plots for theGaussian filters are shown in Figure 6. As can be seen, stackgram filteringpreserves the thickness (or FAHM) of the hotspot better than radial sinogramfiltering, as the kernel width becomes wider. The L-filters, on the other hand,seem to preserve the FAHM in a quite exceptional way in stackgram filtering(see Figure 7). That is, regardless of the employed filter kernel, thethickness of the hotspot remains almost the same. In Figure 7, one point of thestackgram-filtered data seems to be apart from the rest of the quantified datavalues. A possible reason for this might be that the single data pointrepresents a median filter, whereas the other points in the plot representL-filters with a more Gaussian type of weights (9).


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)

Maximum-intensity-value versus FAHM for the compared methods with the nonlinearL-filters. The data points represent the different filter kernels. The filterwidth or length increases from top to bottom. A polynomial of the first degreewas fitted for the three radial filtering data points, whereas a line wasfitted for the quantified stackgram data. Most of the obtained values forradial filtering were omitted from the plot and from the polynomial fit, sincethe values did not fit the appropriate scale. The highest stackgram data valuewas excluded from the line fit. Angular stackgram filtering seems to preservethe resolution (or FAHM) almost perfectly, at the cost of decreasing maximumcount value.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig7: Maximum-intensity-value versus FAHM for the compared methods with the nonlinearL-filters. The data points represent the different filter kernels. The filterwidth or length increases from top to bottom. A polynomial of the first degreewas fitted for the three radial filtering data points, whereas a line wasfitted for the quantified stackgram data. Most of the obtained values forradial filtering were omitted from the plot and from the polynomial fit, sincethe values did not fit the appropriate scale. The highest stackgram data valuewas excluded from the line fit. Angular stackgram filtering seems to preservethe resolution (or FAHM) almost perfectly, at the cost of decreasing maximumcount value.
Mentions: FAHM versus maximum-intensity-value plots for theGaussian filters are shown in Figure 6. As can be seen, stackgram filteringpreserves the thickness (or FAHM) of the hotspot better than radial sinogramfiltering, as the kernel width becomes wider. The L-filters, on the other hand,seem to preserve the FAHM in a quite exceptional way in stackgram filtering(see Figure 7). That is, regardless of the employed filter kernel, thethickness of the hotspot remains almost the same. In Figure 7, one point of thestackgram-filtered data seems to be apart from the rest of the quantified datavalues. A possible reason for this might be that the single data pointrepresents a median filter, whereas the other points in the plot representL-filters with a more Gaussian type of weights (9).

Bottom Line: We discuss data filtering prior to image reconstruction.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.

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.


Related in: MedlinePlus