Spatially adaptive wavelet thresholding of rubidium-82 cardiac PET images

⁸²Rb positron emission tomography (PET) images can be used to diagnose coronary artery disease (CAD), but deterioration of the images due to noise can compromise their quality. This work presents our work on a wavelet-based thresholding method for denoising cardiac ⁸²Rb PET images. Our approach is based on a three-dimensional (3D) discrete dyadic wavelet transform, with iterative thresholding of coefficients being performed in a spatially adaptive context. A subjective analysis indicates that this technique leads to better discrimination between diagnostically significant features and noise when observing the denoised images and the corresponding cardiac polar maps. Results are presented for cardiac PET images of a normal subject (low risk of CAD) using a figure of merit based on expected image properties for such a patient. The increase in image quality is substantial when the proposed method is compared to conventional denoising protocols.