Spatial position association-based 3D CT reconstruction from biplanar X-rays

Abstract

Computed tomography is an advanced medical imaging technique that plays a crucial role in visualizing internal structures. However, it still faces challenges such as high radiation doses and expensive equipment costs. To reduce radiation exposure and improve imaging efficiency, this paper proposes a biplanar reconstruction model called SPAX2CT, which reconstructs high-quality 3D CT volumes from 2D X-ray images. The model incorporates a global frequency-domain fusion module and a local semantic enhancement module. These components strengthen global feature boundaries and compensate for the loss of semantic information in local features. Additionally, SPAX2CT employs spatial position association encoding to capture spatial relationships within the 3D structure, addressing the insufficient spatial position correlation found in traditional methods. Experimental results show that SPAX2CT significantly improves the modeling ability for complex spatial relationships and demonstrates excellent performance on the Lung Image Database Consortium. Our model outperforms baseline methods, while also showing strong scalability and applicability.