  Panoramic X-ray (PX) is a prevalent modality in dental practice for its wide
availability and low cost. However, as a 2D projection image, PX does not
contain 3D anatomical information, and therefore has limited use in dental
applications that can benefit from 3D information, e.g., tooth angular
misa-lignment detection and classification. Reconstructing 3D structures
directly from 2D PX has recently been explored to address limitations with
existing methods primarily reliant on Convolutional Neural Networks (CNNs) for
direct 2D-to-3D mapping. These methods, however, are unable to correctly infer
depth-axis spatial information. In addition, they are limited by the in-trinsic
locality of convolution operations, as the convolution kernels only capture the
information of immediate neighborhood pixels. In this study, we propose a
progressive hybrid Multilayer Perceptron (MLP)-CNN pyra-mid network (3DPX) for
2D-to-3D oral PX reconstruction. We introduce a progressive reconstruction
strategy, where 3D images are progressively re-constructed in the 3DPX with
guidance imposed on the intermediate recon-struction result at each pyramid
level. Further, motivated by the recent ad-vancement of MLPs that show promise
in capturing fine-grained long-range dependency, our 3DPX integrates MLPs and
CNNs to improve the semantic understanding during reconstruction. Extensive
experiments on two large datasets involving 464 studies demonstrate that our
3DPX outperforms state-of-the-art 2D-to-3D oral reconstruction methods,
including standalone MLP and transformers, in reconstruction quality, and also
im-proves the performance of downstream angular misalignment classification
tasks.
