Wavelet-based Denoising of Cardiac PET Data

This thesis focuses on denoising of positron emission tomography (PET) data. Cardiac PET scans generated using a rubidium-82 radiotracer are a convenient, non– invasive method of diagnosing heart disease, but suffer from a high degree of noise. Denoising methods based on the wavelet transform are capable of outperforming existing clinical methods due to their ability to better preserve detail while simultaneously suppressing noise at multiple scales. We investigate the applicability of recently developed wavelet denoising methods to cardiac PET data. A comprehensive set of experiments is performed, in which combinations of these techniques are applied to the different decomposition levels of wavelet coefficients. By doing so, we determine the relevant importance of each (and the domain in which it is applied) to the overall quality of the denoised result. With this information, we propose PET denoising protocols that substantially improve image quality (for static studies) and lead to better measures of myocardial perfusion (for dynamic studies).