A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Noise reduction in PET attenuation correction using non-linear Gaussian filters
Tekijät: Kitamura K, Iida H, Shidahara M, Miura S, Kanno I
Kustantaja: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Julkaisuvuosi: 2000
Journal: IEEE Transactions on Nuclear Science
Tietokannassa oleva lehden nimi: IEEE TRANSACTIONS ON NUCLEAR SCIENCE
Lehden akronyymi: IEEE T NUCL SCI
Vuosikerta: 47
Numero: 3
Aloitussivu: 994
Lopetussivu: 999
Sivujen määrä: 6
ISSN: 0018-9499
DOI: https://doi.org/10.1109/23.856537
Tiivistelmä
In a PET study, shortening of transmission scan time is highly desired for improving patient comfort and increasing scanner throughput. It necessitates a method that reduces statistical noise in attenuation correction factors (ACFs). We have evaluated non-linear Gaussian (NLG) filtering for smoothing transmission images reconstructed with filtered back-projection instead of using iterative reconstruction and segmentation methods. The NLG filtering operation is a variation of local weighted averaging in a neighborhood around a pixel, which weights are determined according to both distance in location and difference in pixel value. Several filtering steps with different NLG parameters can effectively reduce noise without losing structural information. The NLG smoothed transmission images are then forward projected to generate ACFs. Results with phantom and patient data suggested that the NLG filtering method is useful for attenuation correction using count-limited transmission data for both brain and whole-body PET studies.
In a PET study, shortening of transmission scan time is highly desired for improving patient comfort and increasing scanner throughput. It necessitates a method that reduces statistical noise in attenuation correction factors (ACFs). We have evaluated non-linear Gaussian (NLG) filtering for smoothing transmission images reconstructed with filtered back-projection instead of using iterative reconstruction and segmentation methods. The NLG filtering operation is a variation of local weighted averaging in a neighborhood around a pixel, which weights are determined according to both distance in location and difference in pixel value. Several filtering steps with different NLG parameters can effectively reduce noise without losing structural information. The NLG smoothed transmission images are then forward projected to generate ACFs. Results with phantom and patient data suggested that the NLG filtering method is useful for attenuation correction using count-limited transmission data for both brain and whole-body PET studies.
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