  X-ray, known for its ability to reveal internal structures of objects, is
expected to provide richer information for 3D reconstruction than visible
light. Yet, existing neural radiance fields (NeRF) algorithms overlook this
important nature of X-ray, leading to their limitations in capturing structural
contents of imaged objects. In this paper, we propose a framework,
Structure-Aware X-ray Neural Radiodensity Fields (SAX-NeRF), for sparse-view
X-ray 3D reconstruction. Firstly, we design a Line Segment-based Transformer
(Lineformer) as the backbone of SAX-NeRF. Linefomer captures internal
structures of objects in 3D space by modeling the dependencies within each line
segment of an X-ray. Secondly, we present a Masked Local-Global (MLG) ray
sampling strategy to extract contextual and geometric information in 2D
projection. Plus, we collect a larger-scale dataset X3D covering wider X-ray
applications. Experiments on X3D show that SAX-NeRF surpasses previous
NeRF-based methods by 12.56 and 2.49 dB on novel view synthesis and CT
reconstruction. Code, models, and data are released at
https://github.com/caiyuanhao1998/SAX-NeRF
