  Cone beam computed tomography (CBCT) is an important imaging technology
widely used in medical scenarios, such as diagnosis and preoperative planning.
Using fewer projection views to reconstruct CT, also known as sparse-view
reconstruction, can reduce ionizing radiation and further benefit
interventional radiology. Compared with sparse-view reconstruction for
traditional parallel/fan-beam CT, CBCT reconstruction is more challenging due
to the increased dimensionality caused by the measurement process based on
cone-shaped X-ray beams. As a 2D-to-3D reconstruction problem, although
implicit neural representations have been introduced to enable efficient
training, only local features are considered and different views are processed
equally in previous works, resulting in spatial inconsistency and poor
performance on complicated anatomies. To this end, we propose C^2RV by
leveraging explicit multi-scale volumetric representations to enable
cross-regional learning in the 3D space. Additionally, the scale-view
cross-attention module is introduced to adaptively aggregate multi-scale and
multi-view features. Extensive experiments demonstrate that our C^2RV achieves
consistent and significant improvement over previous state-of-the-art methods
on datasets with diverse anatomy.
