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

Division of Cardiology, Department of Medicine, and Department of Radiology, College of Physicians and Surgeons, Columbia University, New York; and Department of Biomedical Engineering, Columbia University, New York, New York

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.

Key Words: quantitative PET • myocardial blood flow • noise reduction • data analysis

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