Dual-Branch Feature Fusion for Sparse-View X-Ray 3D Reconstruction

With the rapid development of medical imaging, sparse-view X-ray 3D reconstruction has become an essential technique for addressing low-dose X-ray imaging challenges. However, due to sparse angular sampling, traditional reconstruction methods often face challenges in handling complex bone and soft tissue structures, leading to information loss and insufficient detail capture. To address these issues, this paper proposes a sparse-view X-ray 3D reconstruction method based on Neural Radiance Fields (NeRF) with a dual-branch feature fusion framework. By synergistically extracting local and global features, this approach enhances the reconstruction of intricate bone and soft tissue structures. For specific applications in regions like the pelvis and aneurism, the method employs depthwise separable convolutions in the local branch to efficiently capture X-ray image details, enhancing the reconstruction of complex bone structures. In the global branch, a pooling Transformer with window mechanisms and hybrid positional encoding is introduced to capture the global features of soft tissue structures like aneurism. Experimental results demonstrate the superiority of this method on multiple medical imaging datasets, particularly in reconstructing complex bone regions and recovering details of soft tissue structures, compared to traditional methods and existing deep learning models.