Improving PET-based physiological quantification through methods of wavelet denoising

The goal of this study was to evaluate methods of multidimensional wavelet denoising on restoring the fidelity of biological signals hidden within dynamic positron emission tomography (PET) images. A reduction of noise within pixels, between adjacent regions, and time-serial frames was achieved via...

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Bibliographic Details
Published in:IEEE transactions on biomedical engineering 2001-02, Vol.48 (2), p.202-212
Main Authors: Jou-Wei Lin, Laine, A.F., Bergmann, S.R.
Format: Article
Language:English
Subjects:
Adult
Algorithms
Biological and medical sciences
Cardiovascular system
Coronary Disease - diagnostic imaging
Data analysis
Diseases
Dynamic tests
Dynamics
Female
Gold
Heart - diagnostic imaging
Humans
Image Enhancement - methods
Image restoration
Imaging
Investigative techniques, diagnostic techniques (general aspects)
Magnetic noise
Maintenance
Male
Medical imaging
Medical research
Medical sciences
Middle Aged
Models, Cardiovascular
Multidimensional systems
Multiscale methods
Myocardial Infarction - diagnostic imaging
NMR
Noise reduction
Nuclear magnetic resonance
Patients
Positron emission tomography
Radionuclide investigations
Reduction
Signal restoration
Tomography, Emission-Computed
Wavelet
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Summary:The goal of this study was to evaluate methods of multidimensional wavelet denoising on restoring the fidelity of biological signals hidden within dynamic positron emission tomography (PET) images. A reduction of noise within pixels, between adjacent regions, and time-serial frames was achieved via redundant multiscale representations. In analyzing dynamic PET data of healthy volunteers, a multiscale method improved the estimate-to-error ratio of flows fivefold without loss of detail. This technique also maintained accuracy of flow estimates in comparison with the "gold standard," using dynamic PET with O15-water. In addition, in studies of coronary disease patients, flow patterns were preserved and infarcted regions were well differentiated from normal regions. The results show that a wavelet-based noise-suppression method produced reliable approximations of salient underlying signals and led to an accurate quantification of myocardial perfusion. The described protocol can be generalized to other temporal biomedical imaging modalities including functional magnetic resonance imaging and ultrasound.
ISSN:0018-9294
1558-2531
DOI:10.1109/10.909641