## 3 Results and Discussion
### Image Reconstruction  
As previously described, each optical channel is characterized by the corresponding SiPM/LED pair position and SDS and its sensitivity is a continuous function of space (banana shape). Hence it is possible to estimate the central position of the region monitored by each channel. Such positions, given the banana-shape light diffusion paths described above, can be approximately located in the middle point in between the particular SiPM/LED pair, at a depth about equal to SDS/2. In fact, for an accurate image reconstruction from the photocurrent data, a rigorous treatment of the near-infrared light transport from the source to the detector and a quite complex data processing are necessary and have to be combined [31; 32; 33; 34; 35]. However, here we want to concentrate on the signals deriving from the hardware, focusing on the achievable SNR and on the elimination of SiPM/LED dishomogeneities and time drifts in realistic conditions. Therefore, we chose to simply and directly investigate the normalized photocurrent signals by considering the geometric arrangement of the SiPM/LED pairs. A software developed in MATLAB(r)provides an arrangement of the collected photocurrents data at the two wavelengths and after the normalization. The data are displayed as a color change of pixels placed on planes classified by the SiPM/LED distances, and taking into account the geometrical positions of sources and detectors, as described in Section 2.3 and in Figure 5, following a back-projection approach [36]. Figures 9-11 report examples of such images at three different time instants to give an idea of the temporal evolution of the images. The pixels represent the measured phantom regions, arranged in a two-dimensional (2D) map, and the pixel color represents the photocurrent normalized to the baseline; the green color represents the baseline, whose value is 1. The value of the color in each pixel at any time is the signal at that time divided by the baseline. Each plane is related to a particular SDS. Clearly, for the lowest SDS, since all the LED/SiPM pairs are involved, the highest number of channels and image pixels is obtained. It should be noted that on each plane some of the pixels do not actually correspond to measurements. For such pixels we attributed a color by performing a linear interpolation (i.e., by assigning them a color given by the average of the closest pixels). This is done only for the pixels with nearest neighbors actually measured. During the bar motion in the phantom, the photocurrents change and, as a consequence, the pixel colors change too. The time sequence of the images of a plane can be visualized as a movie which provides the time evolution of the particular image plane. This can be repeated for each SDS and corresponding plane. Figures 9-11 are, in fact, frames of such movies. From the figures it is evident that the movies of each plane clearly show the localization of the metal bar, whose shape and position are smeared by the light diffusion and its movement. For the planes defined by the SDS = 2 cm, no relevant variation is visible since the rod is moving in the phantom at a depth which is not reached by the banana-shaped photons' path. On the contrary, at SDS = 4.47 cm, the sequence starts to show the passage of a shadow in spatial correspondence with the bar, moving from the right to the left side of the plane. Starting from the 6 cm SDS, the sequence clearly shows the bar passage taking place in the air gap at a depth between 3 cm and 6 cm, below the first EPE layer. Moreover, since at larger SDS values the banana-shaped path of the detected light grows and becomes wider, the moving shadow of the metal bar grows in size as the SDS increases. The signal due to the metal bar motion evidenced in Figures 9-11, of the order of 1% of the baseline (i.e., 20 \(log_{10}(0.01)=40\) dB or somewhat higher), is clearly well detected, although the bar is quite small.