Neighborhood transformer for sparse-view X-ray 3D foot reconstruction

Abstract

In medical imaging, Sparse-View X-ray 3D reconstruction is crucial for analyzing and diagnosing foot bone structures. However, existing methods face limitations when handling sparse view data and complex bone structures. To enhance reconstruction accuracy and detail preservation, this paper proposes an innovative Sparse-View X-ray 3D foot reconstruction technique based on Neighborhood Transformer. A new Neighborhood Position Encoding strategy is introduced, which divides X-ray images into local regions using a window mechanism and precisely selects these regions through nearest neighbor methods, thereby capturing detailed features in the images. Building upon existing NeRF (Neural Radiance Fields) technology, the paper introduces the Neighborhood Transformer module. This module significantly improves the expression capability for complex foot bone structures through depthwise separable convolutions and a dual-branch local–global Transformer network. Additionally, an adaptive weight learning strategy is applied within the Transformer module, enabling the model to better capture long-distance dependencies, thereby improving its ability to handle sparse view data.