Multi-scale and Non Local Mean based filter for Positron Emission Tomography imaging denoising
Hajer Jomaa, Rostom Mabrouk, Frédéric Morain-Nicolier, Nawrès Khlifa · 2016
Dynamic Positron Emission Tomography (PET) is a functional imaging modality which provides information about tracer kinetic in a specific target. In the last three decades, the [18F]-fluorodeoxyglucose ([18F]-FDG) tracer has been widely used by many institutions to measure the local myocardium metabolic rate for glucose. The analysis of the dynamic measurements requires, often, parameters estimation in which the PET data is noisy. In this paper, we propose a systematic methodology to reduce noise in PET data based on the combination of an extension of Non Local Means algorithm and the Discrete Curvelet Transform. The methodology was applied to a small animal model study of the heart, where both the input function and the tissue tracer concentrations at each time were derived from de-noised images. Experimental results revealed a significant improvement in SNR and the spatial distribution of the tracer.