Iterative-based Partial Volume Effects correction with wavelet-based regularization for quantitative PET imaging

Two important technical limitations hampering quantitative measurements with dynamic PET imaging are 1) the limited spatial resolution of the detection system which induces quantitative biases due to Partial Volume Effects and 2), the poor counting statistics in the individual time frames which challenges further the subsequent kinetic modeling and analysis. In this work, we present an original method for the restoration of dynamic PET images that had suffered from both the consequences of the limited spatial resolution and the noise processes, which is based on the weighted least square iterative deconvolution in the wavelet space with temporal regularization of the wavelet coefficients. The solution image exhibits less noise, better contrasts between emitting structures and should therefore allow a better quantification compared with no correction..