Hierarchical Hybrid Transformer for Sparse-View X-Ray 3D Reconstruction

With the continuous advancement of medical imaging technology, sparse-view X-ray 3D reconstruction has found widespread applications in low-dose imaging and rapid scanning. However, traditional reconstruction methods have limitations in capturing details and expressing complex structures. To address this issue, we propose a sparse-view X-ray 3D reconstruction method based on a hierarchical hybrid Transformer. This research uses NeRF as the core framework, combining sparse-view X-ray images. First, a module combining focal attention and multi-scale mixing is employed to partition focal regions, extract multi-scale features, and perform channel attention-based fusion, effectively integrating key regions and hierarchical structural information in the image. This enables the collaborative representation of fine details and global semantics. To address complex anatomical structures such as bones in chest, jaw and foot, as well as soft tissues like aneurism and pancreas, a hierarchical hybrid Transformer module is introduced to model dependencies between local and global regions, enhancing the ability to capture fine details and represent overall structure. Experimental results show that the proposed method achieves significant performance improvements in sparse-view X-ray 3D reconstruction tasks across various medical structures.