Validation of iterative multi-resolution method for partial volume correction and quantification improvement in PET image. (July 2020) - Jomaa, Hajer; Mabrouk, Rostom; Khlifa, Nawres
Highlights: A new strategy of dynamic PET data correction to improve the quantitative information is proposed. The strategy relies on Richardson-Lucy algorithm and a normal inverse Gaussian distribution model in shearlet domain as a regularization step. The method provides notable correction in term of the Myocardium Metabolic Rate for Glucose (MMRGlu) value and efficient noise diminution. Abstract: The partial volume effect (PVE) is a prime limitation that characterizes the Positron Emission Tomography (PET). This effect is mainly caused by the limited spatial resolution and the tissue fraction effect. In fact, the quantification accuracy is significantly affected by the quality of the image. Various post-corrections PVE methods were evaluated in the literature to improve the quantitative information. The deconvolution algorithm is commonly used for partial volume correction (PVC) where no a prior knowledge or information from other modalities expect the PET are invoked. In this context, a new approach based on Richardson-Lucy algorithm is presented in this paper. A normal inverse Gaussian distribution model in shearlet domain was incorporated to the approach as a regularization step. The aim of the proposed method (RL-NIG) is to provide an accurate quantification parameters with an efficient intensity recovery and noise reduction. Experimental results on simulated phantom and preclinical dataset have shown that the present method achieve notable correction in term of the Myocardium Metabolic Rate for Glucose (MMRGlu) value and efficient noise diminution.