Geometry-Aware Spectral Conditioning for Biplanar X-Ray-to-CT Reconstruction

Reconstructing a 3D computed tomography (CT) volume from two orthogonal X-ray projections is severely ill-posed because projection measurements collapse depth information and superimpose anatomical structures. We propose Frequency-Enhanced Dual-Encoding ControlNet (FEDE-ControlNet), a framework that adapts a frozen 3D latent diffusion model for biplanar X-ray-to-CT reconstruction through complementary geometric and spectral conditioning. A geometric pathway back-projects features extracted by a frozen X-ray encoder into a shared volumetric grid, establishing explicit detector-to-volume correspondence. In parallel, a dual-encoding pathway integrates spatial features with global and patch-local Fourier representations through frequency-domain cross-attention, then geometrically lifts the fused features into multi-scale volumetric conditions. A trainable control branch incorporates both pathways through zero-initialized residual connections while keeping the pretrained denoiser and CT autoencoder frozen. To complement latent-space denoising, multi-level volumetric feature supervision aligns decoded predictions with reference CT volumes using hierarchical representations from an independent frozen encoder. This auxiliary supervision is applied only during training and adds no inference-time computation. Experiments demonstrate that the proposed FEDE-ControlNet achieves superior reconstruction fidelity and structural similarity compared with the evaluated state-of-the-art methods.