Furthermore, we evaluated a pixel learning network that receives a single digit as input and consists of a singular extensive fully connected layer, equivalent to the total number of pixels in the image. This network, devoid of convolutional layers, equates to the iterative approach that learns each distinct pixel from a map of ones, utilizing the same training loop as SD2I. The results are compared with those from the SD2I, using a full range 256x400 sinogram, in Figure . It is evident that the presence of multiple fully connected layers and convolutional layers significantly assists the SD2I in producing far more precise and refined results compared to the straightforward pixel learning network.