To validate the reconstruction capabilities of Tomo-NeRF, we conduct four deep learning experiments (Table ). Each experiment used a specific type of synthetic data set as the training data and a specific input to test the algorithm's ability to convert two-, four-and for the model trained with synthetic data comprised of irregular spheres (non-smooth). The first experiment specifically aims to evaluate the practicality of the proposed Tomo-NeRF in real XCT applications. Experiment #2 and #3 use the synthetic data as the input data and utilize algorithms trained with their respective types of data-sets (Table ). These experiments aim to validate the feasibility of the proposed Tomo-NeRF's reconstruction principal and to investigate the the role of geometric complexity (e.g. uniform vs. irregular spheres) on the reconstruction results. Experiment #4 utilizes radiographic images of artificial irregular sphere samples into the model trained by the artificial spherical shape sample dataset.