Panoramic radiography (panoramic X-ray, PX) is a widely used imaging modality
for dental examination. However, its applicability is limited as compared to 3D
Cone-beam computed tomography (CBCT), because PX only provides 2D flattened
images of the oral structure. In this paper, we propose a new framework which
estimates 3D oral structure from real-world PX images. Since there are not many
matching PX and CBCT data, we used simulated PX from CBCT for training,
however, we used real-world panoramic radiographs at the inference time. We
propose a new ray-sampling method to make simulated panoramic radiographs
inspired by the principle of panoramic radiography along with the rendering
function derived from the Beer-Lambert law. Our model consists of three parts:
translation module, generation module, and refinement module. The translation
module changes the real-world panoramic radiograph to the simulated training
image style. The generation module makes the 3D structure from the input image
without any prior information such as a dental arch. Our ray-based generation
approach makes it possible to reverse the process of generating PX from oral
structure in order to reconstruct CBCT data. Lastly, the refinement module
enhances the quality of the 3D output. Results show that our approach works
better for simulated and real-world images compared to other state-of-the-art
methods.Comment: 10 pages, 4 figure