The SD2Vol network begins with a single seed input value (specifically, 1 is used in this work), and is followed by a decoder to reconstruct the image based on the single number. After the input layer, three fully connected layers with 32 nodes and another fully connected layer where the number of nodes equal to the total number of voxels in the reconstructed volume are used. Next, the output of the final fully connected layer is reshaped to a 3D volume followed by four 3D convolutional layers. All activation functions are chosen as ReLU, except for the last layer we used the Leaky ReLU activation function. This architecture can be scaled up, as it allows to reconstruct volumes with n x n x m sizes reaching 550 x 550 x 51 or 100 x 100 x 4010 (i.e. projection data with parallax artefacts). The maximum size of a volume that ParallaxNet can reconstruct with SD2Vol is presented in Supplementary Figure , and Supplementary Tables .