During the training process (see Supporting Information), the encoder-decoder convolutional neural network takes the 0 deg and 90 deg projection images as input and produces a 3D feature grid of probability information that matches the original size of the volumetric grid (100 × 100 × 150). The model samples voxel grids, along X-ray beams among the six projection directions (i.e., beam rotation angle θ). Each direction ranges from 0-180 deg with a step of 30 deg. It sends the query specification of the spatial location along with the input image into the Tomo-NeRF network to retrieve the corresponding feature from the predicted 3D feature grid. The model compares the retrieved 3D prediction along the X-ray beam with the 3D ground truth and has a cross-entropy loss (L CE ) for each grid to learn from the 3D dataset. Tomo-NeRF programs that each X-ray beam in the network may produce a grayscale pixel on the radiographic image under a given projection direction. Therefore, the model can further make a 2D prediction of pixel value by integrating the 3D predictions on the specified X-ray beams with the Beer-Lambert law (Equation ). By comparing the 2D prediction (i.e., a radiographic image from 3D predictions) with the radiography ground truth corresponding to its projection direction, the model can learn from the 2D dataset (i.e., six direction radiographic images for each sample) via mean squared error (MSE) loss.