Use of Wavelet Transforms in Analysis of Time–Activity Data from Cardiac PET

Jou-Wei Lin; Andrew F. Laine; Olakunle Akinboboye; Steven R. Bergmann

Use of Wavelet Transforms in Analysis of Time–Activity Data from Cardiac PET
Lin, Jou-Wei
Laine, Andrew F.
Akinboboye, Olakunle
Bergmann, Steven R.
Biomedical Engineering
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Journal of Nuclear Medicine
Because of its intrinsic quantitative properties, PET permits measurement of myocardial perfusion and metabolism in absolute terms (i.e., mL/g/min). However, quantification has been limited by errors produced in image acquisition, selection of regions of interest, and data analysis. The goal of this study was to evaluate a newly developed, novel, wavelet-based noise-reduction approach that can objectively extract biologic signals hidden within dynamic PET data. Methods: Quantification of myocardial perfusion using dynamic PET imaging with 82Rb, H215O, and 13NH3 was selected to evaluate the effects of the wavelet-based noise-reduction protocol. Dynamic PET data were fitted to appropriate mathematic models before and after wavelet-based noise reduction to get flow estimates. Time–activity curves, precision, accuracy, and differentiating capacity derived from the wavelet protocol were compared with those obtained from unmodified data processing. A total of 84 human studies was analyzed, including 43 at rest (18 82Rb scans, 18 H215O scans, and 7 13NH3 scans) and 41 after coronary hyperemia with dipyridamole (17 82Rb scans, 17 H215O scans, and 7 13NH3 scans). Results: For every tracer tested under all conditions, the wavelet method improved the shape of blood and tissue time–activity curves, increased estimate-to-error ratios, and maintained fidelity of flow in regions as small as 0.85 cm3. It also improved the accuracy of flow estimates derived from 82Rb to the level of that achieved with H215O, which was not affected markedly by the wavelet process. In studies of patients with coronary disease, regional heterogeneity of myocardial perfusion was preserved and flow estimates in infarcted regions were differentiated more easily from normal regions. Conclusion: The wavelet-based noise-reduction method effectively and objectively extracted tracer time–activity curves from data with low signal-to-noise ratios and improved the accuracy and precision of measurements with all tracer techniques studied. The approach should be generalizable to other image modalities such as functional MRI and CT and, therefore, improve the ability to quantify dynamic physiologic processes.
Biomedical engineering
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Suggested Citation:
Jou-Wei Lin, Andrew F. Laine, Olakunle Akinboboye, Steven R. Bergmann, , Use of Wavelet Transforms in Analysis of Time–Activity Data from Cardiac PET, Columbia University Academic Commons, .

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