Oral-NeXF: 3D Oral Reconstruction with Neural X-ray Field from Panoramic Imaging
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3D reconstruction of medical images from 2D images has increasingly become a challenging research topic with the advanced development of deep learning methods. Previous work in 3D reconstruction from limited (generally one or two) X-ray images mainly relies on learning from paired 2D and 3D images. In 3D oral reconstruction from panoramic imaging, the model also relies on some prior individual information, such as the dental arch curve or voxel-wise annotations, to restore the curved shape of the mandible during reconstruction. These limitations have hindered the use of single X-ray tomography in clinical applications. To address these challenges, we propose a new model that relies solely on projection data, including imaging direction and projection image, during panoramic scans to reconstruct the 3D oral structure. Our model builds on the neural radiance field by introducing multi-head prediction, dynamic sampling, and adaptive rendering, which accommodates the projection process of panoramic X-ray in dental imaging. Compared to end-to-end learning methods, our method achieves state-of-the-art performance without requiring additional supervision or prior knowledge.
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