Noise reduction in PET attenuation correction using non-linear Gaussian filters

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). The authors have evaluated non-linear Gaussian (NLG) filtering for...

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Bibliographic Details
Published in:IEEE transactions on nuclear science 2000-06, Vol.47 (3), p.994-999
Main Authors: Kitamura, K., Iida, H., Shidahara, M., Miura, S., Kanno, I.
Format: Article
Language:English
Subjects:
Attenuation
Brain
Digital filters
Filtering
Filtration
Image reconstruction
Image segmentation
Information filtering
Information filters
Iterative methods
Noise reduction
Nonlinearity
Patients
Pixels
Positron emission
Positron emission tomography
Smoothing methods
Throughput
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Summary: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). The authors 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.
ISSN:0018-9499
1558-1578
DOI:10.1109/23.856537