Gaussian Filter for Brain SPECT Imaging
Background. The presence of a noise component on 3D images of single-photon emission computed tomography (SPECT) of a brain significantly distorts the probability distribution function (PD) of the radioactive count rate in the images. The presence of noise and further filtering of the data, based...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Igor Sikorsky Kyiv Polytechnic Institute
2022-02-01
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| Series: | Innovative Biosystems and Bioengineering |
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| Online Access: | http://ibb.kpi.ua/article/view/128475 |
| _version_ | 1818026263119396864 |
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| author | Nikolay Nikolov Sergiy Makeyev Olga Korostynska Tetyana Novikova Yelizaveta Kriukova |
| author_facet | Nikolay Nikolov Sergiy Makeyev Olga Korostynska Tetyana Novikova Yelizaveta Kriukova |
| author_sort | Nikolay Nikolov |
| collection | DOAJ |
| description |
Background. The presence of a noise component on 3D images of single-photon emission computed tomography (SPECT) of a brain significantly distorts the probability distribution function (PD) of the radioactive count rate in the images. The presence of noise and further filtering of the data, based on a subjective assessment of image quality, have a significant impact on the calculation of volumetric cerebral blood flow and the values of the uptake asymmetry of the radiopharmaceutical in a brain.
Objective. We are aimed to develop a method for optimal SPECT filtering of brain images with lipophilic radiopharmaceuticals, based on a Gaussian filter (GF), for subsequent image segmentation by the threshold method.
Methods. SPECT images of the water phantom and the brain of patients with 99mTc-HMPAO were used. We have developed a technique for artificial addition of speckle noise to conditionally flawless data in order to determine the optimal parameters for smoothing SPECT, based on a GF. The quantitative criterion for optimal smoothing was the standard deviation between the PD of radioactive count rate of the smoothed image and conditionally ideal one.
Results. It was shown that the maximum radioactive count rate of the SPECT image has an extremum by changing the standard deviation of the GF in the range of 0.3–0.4 pixels. The greater the noise component in the SPECT image, the more quasi-linearly the corresponding rate changes. This dependence allows determining the optimal smoothing parameters. The application of the developed smoothing technique allows restoring the probability distribution function of the radioactive count rate (distribution histogram) with an accuracy up to 5–10%. This provides the possibility to standardize SPECT images of brain.
Conclusions. The research results of work solve a specific applied problem: restoration of the histogram of a radiopharmaceuticals distribution in a brain for correct quantitative assessment of regional cerebral blood flow. In contrast to the well-known publications on the filtration of SPECT data, the work takes into account that the initial tomographic data are 3D, rather than 2D slices, and contain not only uniform random Gaussian noise, but also a pronounced speckle component.
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| first_indexed | 2024-12-10T04:29:13Z |
| format | Article |
| id | doaj.art-47a7b5340bec4accaf4b39f42033e4bd |
| institution | Directory Open Access Journal |
| issn | 2616-177X |
| language | English |
| last_indexed | 2024-12-10T04:29:13Z |
| publishDate | 2022-02-01 |
| publisher | Igor Sikorsky Kyiv Polytechnic Institute |
| record_format | Article |
| series | Innovative Biosystems and Bioengineering |
| spelling | doaj.art-47a7b5340bec4accaf4b39f42033e4bd2022-12-22T02:02:11ZengIgor Sikorsky Kyiv Polytechnic InstituteInnovative Biosystems and Bioengineering2616-177X2022-02-016110.20535/ibb.2022.6.1.128475Gaussian Filter for Brain SPECT ImagingNikolay Nikolov0Sergiy Makeyev1Olga Korostynska2Tetyana Novikova3Yelizaveta Kriukova4Igor Sikorsky Kyiv Polytechnic Institute; Kundiiev Institute of Occupational Health, NAMS of UkraineRomodanov Neurosurgery Institute, NAMS of UkraineOslo Metropolitan UniversityRomodanov Neurosurgery Institute, NAMS of UkraineIgor Sikorsky Kyiv Polytechnic Institute Background. The presence of a noise component on 3D images of single-photon emission computed tomography (SPECT) of a brain significantly distorts the probability distribution function (PD) of the radioactive count rate in the images. The presence of noise and further filtering of the data, based on a subjective assessment of image quality, have a significant impact on the calculation of volumetric cerebral blood flow and the values of the uptake asymmetry of the radiopharmaceutical in a brain. Objective. We are aimed to develop a method for optimal SPECT filtering of brain images with lipophilic radiopharmaceuticals, based on a Gaussian filter (GF), for subsequent image segmentation by the threshold method. Methods. SPECT images of the water phantom and the brain of patients with 99mTc-HMPAO were used. We have developed a technique for artificial addition of speckle noise to conditionally flawless data in order to determine the optimal parameters for smoothing SPECT, based on a GF. The quantitative criterion for optimal smoothing was the standard deviation between the PD of radioactive count rate of the smoothed image and conditionally ideal one. Results. It was shown that the maximum radioactive count rate of the SPECT image has an extremum by changing the standard deviation of the GF in the range of 0.3–0.4 pixels. The greater the noise component in the SPECT image, the more quasi-linearly the corresponding rate changes. This dependence allows determining the optimal smoothing parameters. The application of the developed smoothing technique allows restoring the probability distribution function of the radioactive count rate (distribution histogram) with an accuracy up to 5–10%. This provides the possibility to standardize SPECT images of brain. Conclusions. The research results of work solve a specific applied problem: restoration of the histogram of a radiopharmaceuticals distribution in a brain for correct quantitative assessment of regional cerebral blood flow. In contrast to the well-known publications on the filtration of SPECT data, the work takes into account that the initial tomographic data are 3D, rather than 2D slices, and contain not only uniform random Gaussian noise, but also a pronounced speckle component. http://ibb.kpi.ua/article/view/128475emission computed tomographySPECTcerebral blood flowradioactive countGaussian filteroptimal filtering |
| spellingShingle | Nikolay Nikolov Sergiy Makeyev Olga Korostynska Tetyana Novikova Yelizaveta Kriukova Gaussian Filter for Brain SPECT Imaging Innovative Biosystems and Bioengineering emission computed tomography SPECT cerebral blood flow radioactive count Gaussian filter optimal filtering |
| title | Gaussian Filter for Brain SPECT Imaging |
| title_full | Gaussian Filter for Brain SPECT Imaging |
| title_fullStr | Gaussian Filter for Brain SPECT Imaging |
| title_full_unstemmed | Gaussian Filter for Brain SPECT Imaging |
| title_short | Gaussian Filter for Brain SPECT Imaging |
| title_sort | gaussian filter for brain spect imaging |
| topic | emission computed tomography SPECT cerebral blood flow radioactive count Gaussian filter optimal filtering |
| url | http://ibb.kpi.ua/article/view/128475 |
| work_keys_str_mv | AT nikolaynikolov gaussianfilterforbrainspectimaging AT sergiymakeyev gaussianfilterforbrainspectimaging AT olgakorostynska gaussianfilterforbrainspectimaging AT tetyananovikova gaussianfilterforbrainspectimaging AT yelizavetakriukova gaussianfilterforbrainspectimaging |