Tomo-NeRF is a learning framework that can reconstruct 3D images from a limited number of fixed radiographic projection views. Neural radiance field (NeRF) was previously developed for novel photorealistic views synthesis with an incomplete set of photos. It leverages a continuous volumetric radiance field of color and density for volumetric scene prediction and uses gradient descent to optimize the scene using the input photos. Our Tomo-NeRF employs the convolutional approach to the conventional NeRF, pixelnerf. Convolutional layers in convolutional neural networks (CNNs) utilize a local receptive field defined by the size of the convolutional kernel. This design enhances network's flexibility and computational efficiency when extracting image features, compared to fully connected structure.