SPIDER: Structure-Preferential Implicit Deep Network for Biplanar X-ray Reconstruction
Journal:
arXiv
Published Date:
Jul 7, 2025
Abstract
Biplanar X-ray imaging is widely used in health screening, postoperative
rehabilitation evaluation of orthopedic diseases, and injury surgery due to its
rapid acquisition, low radiation dose, and straightforward setup. However, 3D
volume reconstruction from only two orthogonal projections represents a
profoundly ill-posed inverse problem, owing to the intrinsic lack of depth
information and irreducible ambiguities in soft-tissue visualization. Some
existing methods can reconstruct skeletal structures and Computed Tomography
(CT) volumes, they often yield incomplete bone geometry, imprecise tissue
boundaries, and a lack of anatomical realism, thereby limiting their clinical
utility in scenarios such as surgical planning and postoperative assessment. In
this study, we introduce SPIDER, a novel supervised framework designed to
reconstruct CT volumes from biplanar X-ray images. SPIDER incorporates tissue
structure as prior (e.g., anatomical segmentation) into an implicit neural
representation decoder in the form of joint supervision through a unified
encoder-decoder architecture. This design enables the model to jointly learn
image intensities and anatomical structures in a pixel-aligned fashion. To
address the challenges posed by sparse input and structural ambiguity, SPIDER
directly embeds anatomical constraints into the reconstruction process, thereby
enhancing structural continuity and reducing soft-tissue artifacts. We conduct
comprehensive experiments on clinical head CT datasets and show that SPIDER
generates anatomically accurate reconstructions from only two projections.
Furthermore, our approach demonstrates strong potential in downstream
segmentation tasks, underscoring its utility in personalized treatment planning
and image-guided surgical navigation.
