## 9 Results and Discussion
### Comparison of the Data Formats  
Before analyzing the results of the different data format configurations with the pre-selected metrics, we performed a human visual analysis of the CTP401 module. In the human visual analysis, we observed that the pre-processing step was well performed and the grid between sensors was removed. Figure 15 shows the 2D projections pre- and post-processing with the different data format configurations. Yet, we did not observe differences between the different configurations with the human visual analysis. The human visual analysis was conducted also for the reconstructed images, shown in Figure 16. We observed that all target materials can be distinguished independently by the  
Figure 16: Reconstructed image of the CTP401 module for human visual analysis. Figure 15: Single projection of module CTP401, before and after the pre-processing. data format configuration utilized in the pre-processing step. This means that the image quality does not seem to change in terms of human visual analysis. Since with the human visual analysis, it is not possible to compare the accuracy and the information lost between the different data formats, we estimated the MSE of the projections. The MSE, as explained in Section 7, is the key point used for reducing the eligible data formats for the DSE. It was crucial to select the two data format configurations of the 16-bit and 32-bit fixed-point; we reduced from 48 to 2 possible data format configurations. The MSE is a reasonable metric in this step because it can be applied to 2D projections before the reconstruction. In addition, we noticed that it is in the same order of magnitude for the different phantoms. However, the MSE does not consider all image quality metrics, which are significant to understanding how data formats influence the quality of reconstructed images. Therefore, we performed the measurement of _low contrast_, _noise_ and _uniformity_ for the reconstructed images. For calculating these metrics, we acquired, pre-processed with different data format configurations, and reconstructed the modules shown in Figures 17 and 18. The results of the image quality analysis are shown in Table 3. We observed that the MSE of all configurations is lower than 1. This value is very good in terms of image quality since the MSE is usually between 2.36 and 2.37 also in medical image compression [51]. By comparing the other image quality metrics of all data formats with the double format, the values were judged to be good in most of the cases. In fact, these are in the same order of magnitude as shown in Table 3. The only data format where the approximation of the pre-processing has slightly influenced the reconstructed images is the 16-bit fixed point. Due to the approximation made and the bit truncation involved for converting the raw sensor data from 16-bit unsigned to 16-bit fixed point, the low contrast pixels are blurred. Therefore, the contrast noise ratio (CNR) is about 0.2 lower than the double configuration. By contrast, due to the blurred low contrast pixels of the 16-bit fixed-point configuration, a lower _noise_ and a better _uniformity_ was estimated for this configuration, compared to other data formats. In fact, in images with blurred pixels, the pixel values are similar, and therefore, there is a lower _noise_ and a better _uniformity_. From this image quality analysis, we can conclude that all data formats result in an acceptable pre-processed and reconstructed image quality. The 16-bit fixed point has some problems that would make sense to choose it only if the hardware cost has much advantages, compared to other solutions. To define the hardware costs of the different data types, we compared the resource utilization and the latency of the various data format configurations. As shown in Table 4, the utilization of all FPGA resources decreases from floating-point to fixed-point representations, independently of the data width.