  3D reconstruction of medical imaging from 2D images has become an
increasingly interesting topic with the development of deep learning models in
recent years. Previous studies in 3D reconstruction from limited X-ray images
mainly rely on learning from paired 2D and 3D images, where the reconstruction
quality relies on the scale and variation of collected data. This has brought
significant challenges in the collection of training data, as only a tiny
fraction of patients take two types of radiation examinations in the same
period. Although simulation from higher-dimension images could solve this
problem, the variance between real and simulated data could bring great
uncertainty at the same time. In oral reconstruction, the situation becomes
more challenging as only a single panoramic X-ray image is available, where
models need to infer the curved shape by prior individual knowledge. To
overcome these limitations, we propose Oral-3Dv2 to solve this cross-dimension
translation problem in dental healthcare by learning solely on projection
information, i.e., the projection image and trajectory of the X-ray tube. Our
model learns to represent the 3D oral structure in an implicit way by mapping
2D coordinates into density values of voxels in the 3D space. To improve
efficiency and effectiveness, we utilize a multi-head model that predicts a
bunch of voxel values in 3D space simultaneously from a 2D coordinate in the
axial plane and the dynamic sampling strategy to refine details of the density
distribution in the reconstruction result. Extensive experiments in simulated
and real data show that our model significantly outperforms existing
state-of-the-art models without learning from paired images or prior individual
knowledge. To the best of our knowledge, this is the first work of a
non-adversarial-learning-based model in 3D radiology reconstruction from a
single panoramic X-ray image.
