The architecture of the artificial neural network used for reconstructing the images from the sinograms is depicted in Figure . There are two novelties in this design compared to other architectures previously proposed for tomographic image reconstruction. First, the SD2I network, as the name suggests, starts from a single neuron rather than a 2D image which significantly reduces the number of parameters in the architecture. In other networks, the input is a 2D image which is either flattened and connected to a dense layer containing 100s of neurons (e.g. 256 in the GANrec) or is followed by a series of 2D convolutional and downsampling layers with the final layer being flattened and connected to the aforementioned dense layer. The second novelty is related to the large dense layer and the convolutional layers that are connected to it which dramatically reduces the number of parameters in the network's architecture. In this paper, we are going to use two types of SD2I architectures which are called the SD2I and the SD2I with upsampling layers (SD2Iu) respectively. The SD2I presented in Figure shows an architecture that receives a single number as input and has a large fully connected layer in the middle. As this architecture lacks the encoder network present in both GANrec and AUTOMAP architectures, SD2I can allocate more parameters to augment the decoding network's size. Consequently, under the same model size constraints, SD2I is capable of reconstructing images with higher quality than GANrec and AUTOMAP. The impact of k factors on the performance of the SD2I network is also presented in Figure and Table . Simultaneously, the SD2Iu architecture depicted in Figure possesses fewer parameters than the SD2I architecture, achieved by reducing the size of the fully connected layers. The network initially predicts an image at a lower resolution and subsequently upscales it to the original image size through the use of upsampling and convolutional layers. To clarify, the initial single neuron is followed by three small dense layers, each containing 64 neurons. This third small dense layer is then connected to a larger dense layer consisting of (m/4)×(m/4)×k neurons, where m is the number of pixels in one dimension of the fully reconstructed images which have size equal to m×m. The k factor is an integer and increasing it can lead to better performance of the neural network but also increases the number of parameters. In this work and after initial testing, we used a range of k between 4 and 8; this range provides a good balance between network size/ training speed and quality of reconstructed images (Figure ). The large dense layer is then reshaped to a 2D layer of size, followed by an upsampling layer resulting in an image with size of (m/2, m/2). This is followed by three 2D convolutional layers and a second upsampling layer resulting in an image with size of (m, m). Each of these 2D convolutional layers has 64 filters with a kernel size of 3 and stride equal to 1. The final layer of the architecture is a 2D convolutional layer with one kernel (kernel size of 3) and stride equal to 1. We employed ReLU as the activation function for all hidden layers, except the final output layer. To determine the most suitable activation function for the output layer, we assessed the performance of various alternatives and ultimately selected the absolute function as our choice for the output layer . The performance comparison of the various activation functions is illustrated in Figure and Table . Our findings indicate that the absolute function outperforms the rest. Although ReLU could be considered as a potential alternative, we experienced numerous dead-pixel issues when using it, particularly with experimental data containing noise so for all results presented in this work we used the absolute function.