D3T: Dual-Domain Diffusion Transformer in Triplanar Latent Space for 3D Incomplete-View CT Reconstruction

Xuhui Liu, Hong Li, Zhi Qiao, Yawen Huang, Xi Liu, Juan Zhang, Zhen Qian, Xiantong Zhen, Baochang Zhang

Abstract: Computed tomography (CT) is a cornerstone of clinical imaging, yet its accessibility in certain scenarios is constrained by radiation exposure concerns and operational limitations within surgical environments. CT reconstruction from incomplete views has attracted increasing research attention due to its great potential in medical applications. However, it is inherently an ill-posed problem, which, coupled with the complex, high-dimensional characteristics of 3D medical data, poses great challenges such as artifact mitigation, global incoherence, and high computational costs. To tackle those challenges, this paper introduces D3T, a new 3D conditional diffusion transformer that models 3D CT distributions in the low-dimensional 2D latent space for incomplete-view CT reconstruction. Our approach comprises two primary components: a triplanar vector quantized auto-encoder (TriVQAE) and a latent dual-domain diffusion transformer (LD3T). TriVQAE encodes high-resolution 3D CT images into compact 2D latent triplane codes which effectively factorize the intricate CT structures, further enabling compute-friendly diffusion model architecture design. Operating in the latent triplane space, LD3T significantly reduces the complexity of capturing the intricate structures in CT images. Its improved diffusion transformer architecture efficiently understands the global correlations across the three planes, ensuring high-fidelity 3D reconstructions. LD3T presents a new dual-domain conditional generation pipeline that incorporates both image and projection conditions, facilitating controllable reconstruction to produce 3D structures consistent with the given conditions. Moreover, LD3T introduces a new Dual-Space Consistency Loss that integrates image-level supervision beyond standard supervision in the latent space to enhance consistency in the 3D image space. Extensive experiments on four datasets with three inverse settings demonstrate the effectiveness of our proposal.

External IDs:dblp:journals/ijcv/LiuLQHLZQZZ25