Multi-view Diffusion Model with Gaussian Splatting in Projection Domain for Limited Angle Reconstruction

Industrial computed tomography (CT) plays a critical role in non-destructive testing and quality control; however, limited-angle acquisition often leads to severe artifacts due to restricted scanning rotation. To address this issue, we propose a novel framework that exploits the generative capability of a video diffusion model for multi-view generation in the projection domain and uses a novel reconstruction method. Specifically, a well-trained video diffusion model uses the available projections from limited-angle tomography as conditions to generate the missing-angle projections. To further enhance reconstruction quality and reduce the information loss introduced by traditional methods such as the Feldkamp–Davis–Kress (FDK) algorithm, we integrate a Gaussian splatting–based 3D reconstruction method, enabling volumetric reconstruction from the generated projections. Based on our experimental results, the proposed framework can generate novel projections of size 256 x 256 pixel in approximately 1 minute and complete the subsequent 3D reconstruction in about 5 minutes, offering new opportunities for inline CT applications. Experimental results demonstrate that novel-view generation substantially improves reconstruction performance, achieving significant gains in MSE, PSNR, and SSIM compared with conventional limited-angle tomography.