MAC-DiffCT: Multi-Scale Adaptive Conditional Diffusion Model for CT Reconstruction from Biplanar X-rays

Sparse-view CT reconstruction aims to synthesize volumetric CT images from a limited number of X-ray projections, reducing radiation dose while maintaining diagnostic quality. However, the substantial cross-modal gap between 2D X-rays and 3D CT volumes presents significant challenges, including uneven distribution of information across different views, artifact issues, and excessive computational costs. Therefore, in this paper, we propose MAC-DiffCT, a multi-scale adaptive conditional diffusion model designed for accurate and efficient CT reconstruction from biplanar X-rays. Our approach first extracts multi-scale 2D features from multi-view X-rays using a UNet-based encoder. A novel Bi-Directional Cross-Attention (BDC-Att) module adaptively fuses features by assigning spatially varying weights to each view. We then introduce a Multi-Scale Feature Sampling (MS-FS) module that projects 3D coordinates onto 2D planes, sample features across scales, and integrates them via a multi-layer perceptron to form a latent structural representation. This 3D structural feature serves as a condition for a latent-space conditional diffusion model, which reconstructs high-quality CT volumes with enhanced anatomical fidelity. An additional signed distance function (SDF) loss is applied to promote structural consistency in the 3D space. Experimental results on both public and private chest datasets demonstrate that MAC-DiffCT consistently outperforms existing methods, achieving the highest reconstruction accuracy with PSNR of 27.68 and 25.43 dB, SSIM of 0.8223 and 0.7598, and the lowest LPIPS of 0.0992 and 0.1322. Downstream evaluation via lung segmentation and an interpretability study further highlight the transparent, explainable, and anatomically grounded nature of our model. MAC-DiffCT offers a low-radiation alternative to conventional CT, especially for vulnerable patients requiring repeated imaging and intraoperative scenarios where CT use is constrained.