  Sparse-view cone-beam CT (CBCT) reconstruction is an important direction to
reduce radiation dose and benefit clinical applications. Previous voxel-based
generation methods represent the CT as discrete voxels, resulting in high
memory requirements and limited spatial resolution due to the use of 3D
decoders. In this paper, we formulate the CT volume as a continuous intensity
field and develop a novel DIF-Net to perform high-quality CBCT reconstruction
from extremely sparse (fewer than 10) projection views at an ultrafast speed.
The intensity field of a CT can be regarded as a continuous function of 3D
spatial points. Therefore, the reconstruction can be reformulated as regressing
the intensity value of an arbitrary 3D point from given sparse projections.
Specifically, for a point, DIF-Net extracts its view-specific features from
different 2D projection views. These features are subsequently aggregated by a
fusion module for intensity estimation. Notably, thousands of points can be
processed in parallel to improve efficiency during training and testing. In
practice, we collect a knee CBCT dataset to train and evaluate DIF-Net.
Extensive experiments show that our approach can reconstruct CBCT with high
image quality and high spatial resolution from extremely sparse views within
1.6 seconds, significantly outperforming state-of-the-art methods. Our code
will be available at https://github.com/xmed-lab/DIF-Net.
