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Deep learning (DL) has emerged as a new approach in the field of computedtomography (CT) with many applicaitons. A primary example is CT reconstructionfrom incomplete data, such as sparse-view image reconstruction. However,applying DL to sparse-view cone-beam CT (CBCT) remains challenging. Many modelslearn the mapping from sparse-view CT images to the ground truth but often failto achieve satisfactory performance. Incorporating sinogram data and performingdual-domain reconstruction improve image quality with artifact suppression, buta straightforward 3D implementation requires storing an entire 3D sinogram inmemory and many parameters of dual-domain networks. This remains a majorchallenge, limiting further research, development and applications. In thispaper, we propose a sub-volume-based 3D denoising diffusion probabilistic model(DDPM) for CBCT image reconstruction from down-sampled data. Our DDPM network,trained on data cubes extracted from paired fully sampled sinograms anddown-sampled sinograms, is employed to inpaint down-sampled sinograms. Ourmethod divides the entire sinogram into overlapping cubes and processes them inparallel on multiple GPUs, successfully overcoming the memory limitation.Experimental results demonstrate that our approach effectively suppressesfew-view artifacts while preserving textural details faithfully.

Sub-volume-based Denoising Diffusion Probabilistic Model for Cone-beam CT Reconstruction from Incomplete Data