## 2 Materials and Methods
### Study of Rat Behavior in PET Scanner and How Count Subtraction Affects the Image  
The objective of this section is to investigate the effects of rat behavior on PET imaging data, particularly focusing on the impact of subtracting coincidences from the acquisition, as shown in steps 2 and 4 in Figure 1.  
To assess the effects of animal behavior on PET imaging data, we conducted a comprehensive study using four different Wistar rats injected with \({}^{18}\)F-FDG within the 6R-SuperArgus scanner. During the experiments, the rats were awake. The rats were introduced into a tube that offered freedom of movement. However, the limited diameter of the tube prevented the animals from making full turns, thereby ensuring that they remained within the FOV of the scanner. Acquisitions lasted approximately 600 s for each rat. A primary objective was to identify the parts of the acquisition where the rats moved too quickly, as such movements can adversely affect image quality. Consequently, we subtracted these fastest motion data from the final image reconstruction to improve the overall accuracy.  
In one of the rats, four point sources of \({}^{18}\)F were placed at the same positions as the simulation shown in Figure 2. This rat is a wistar female rat weighing 255 g. Each point source had an activity of 7 uCi, while the rat's brain had a total activity of 110 uCi at the beginning of the acquisition. Figure 3 shows the rat with the point sources. The study focused on exploring the effects of subtracting more or fewer coincidences by varying the parameters in the reconstruction process. The two parameters that significantly impact the number of counts are \(v_{max}\) and \(\chi^{2}_{max}\), as discussed earlier.  
Figure 2: Schematic representation of the numerical rat phantom used in our simulations [26], located inside the 6R-SuperArgus PET scanner. The phantom includes four point sources, two at the snout and two under the ear. The point sources are shown larger than their actual size (1 mm diameter) for visualization purposes.  
Two different metrics were used to assess the effect of these parameters and the subtraction of counts. First, we used the point sources attached to the animal's brain to measure the precision of motion compensation by calculating the Full Width at Half Maximum (FWHM) of these sources. The FWHM is a useful measure of spatial resolution that allows us to assess the amount of blurring caused by movement during acquisition.  
Secondly, we used the cortex region to measure the signal-to-noise ratio (SNR), which provides valuable insight into the impact of count subtraction on image quality. SNR is defined by the following formula:  
\[\text{SNR}=\frac{\mu}{std} \tag{4}\]  
Here, \(\mu\) represents the mean value inside the region of the cortex, while \(std\) denotes the standard deviation within the same region. A higher SNR indicates better image quality with reduced noise.  
By analyzing the FWHM and SNR under different conditions, we aim to understand how the movement of rats during PET acquisitions affects image quality and how count subtraction influences the final reconstruction. These insights will contribute to the optimization of point source tracking in Awake Rat PET Imaging, leading to more accurate and reliable data for neuroscience research and other related fields.