Abstract
Limited-angle cardiac CT reconstruction is a severely ill-posed problem, where incomplete angular coverage leads to strong artifacts and structural distortions. Although diffusion-based methods have shown strong potential for improving reconstruction quality, their high computational cost, large memory demand, and slow inference remain major barriers to practical clinical deployment. To address this bottleneck, we propose WDK-Net, a lightweight dual-domain reconstruction framework with a structure-detail decoupled design, aiming to make diffusion-based reconstruction more computationally feasible for cardiac CT. Leveraging wavelet transforms, WDK-Net models global structures and local details in different domains. Specifically, although shallow low-frequency components preserve more complete anatomical structures, deeper decomposition provides a more compact low-frequency representation for efficient generative modeling. Experiments on simulated, in-house clinical, and public cardiac CT datasets demonstrate that the proposed method achieves competitive or superior reconstruction quality across multiple limited-angle settings (60°, 90°, 120°), while substantially reducing the computational burden compared with existing diffusion-based reconstruction methods. These results suggest that frequency-selective low-dimensional wavelet diffusion, combined with explicit structure-detail decoupling, provides a feasible pathway toward more efficient and clinically deployable diffusion-based limited-angle cardiac CT reconstruction.