SPIDER: Structure-Preferential Implicit Deep Network for Biplanar X-ray Reconstruction

TL;DR

SPIDER is a supervised neural network framework that reconstructs accurate 3D CT volumes from only two orthogonal X-ray images by integrating anatomical priors and implicit representations, improving clinical utility.

Contribution

This work introduces SPIDER, a novel method that embeds anatomical constraints into neural reconstruction, enhancing 3D head CT reconstruction from minimal X-ray views.

Findings

Achieves anatomically accurate head CT reconstructions from two X-ray projections.

Enhances downstream segmentation performance, aiding surgical planning.

Reduces soft-tissue artifacts and improves structural continuity.

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.