  Cone Beam Computed Tomography (CBCT) is the most widely used imaging method
in dentistry. As hundreds of X-ray projections are needed to reconstruct a
high-quality CBCT image (i.e., the attenuation field) in traditional
algorithms, sparse-view CBCT reconstruction has become a main focus to reduce
radiation dose. Several attempts have been made to solve it while still
suffering from insufficient data or poor generalization ability for novel
patients. This paper proposes a novel attenuation field encoder-decoder
framework by first encoding the volumetric feature from multi-view X-ray
projections, then decoding it into the desired attenuation field. The key
insight is when building the volumetric feature, we comply with the multi-view
CBCT reconstruction nature and emphasize the view consistency property by
geometry-aware spatial feature querying and adaptive feature fusing. Moreover,
the prior knowledge information learned from data population guarantees our
generalization ability when dealing with sparse view input. Comprehensive
evaluations have demonstrated the superiority in terms of reconstruction
quality, and the downstream application further validates the feasibility of
our method in real-world clinics.
