Evaluation of the Respiratory Motion Effect in Small Animal PET Images with GATE Monte Carlo Simulations

Abstract

The rapid growth in genetics and molecular biology combined with the development of techniques for genetically engineering small animals has led to increased interest in in vivo small animal imaging (Cherry & Gambhir, 2001; Riemann et al., 2008; Rowland & R., 2008). Small animal imaging has been applied frequently to the imaging of small animals (mice and rats), which are ubiquitous in modeling human diseases and testing treatments. The use of PET in small animals allows the use of subjects as their own control, reducing the interanimal variability. This allows performing longitudinal studies on the same animal and improves the accuracy of biological models (Cherry, 2004). However, small animal PET still suffers from several limitations. The amounts of radiotracers needed, limited scanner sensitivity, image resolution and image quantification issues, all could clearly benefit from additional research (Chatziioannou, 2002; Tai & Laforest, 2005). Because nuclear medicine imaging deals with radioactive decay, the emission of radiation energy through photons and particles alongside with the detection of these quanta and particles in different materials make Monte Carlo method an important simulation tool in both nuclear medicine research and clinical practice. In order to optimize the quantitative use of PET in clinical practice, dataand image-processing methods are also a field of intense interest and development. The evaluation of such methods often relies on the use of simulated data and images since these offer control of the ground truth. Monte Carlo simulations are widely used for PET simulation since they take into account all the random processes involved in PET imaging, from the emission of the positron to the detection of the photons by the detectors. Simulation techniques have become an importance and indispensable complement to a wide range of problems that could not be addressed by experimental or analytical approaches (Rogers, 2006). Monte Carlo methods are numerical calculation methods based on random variable sampling. This approach has been used to solve mathematical problems since 1770 and has been named “Monte Carlo” by Von Neumann because of the similarity of statistical simulations to games of chance, represented by the most well known center for gambling: