Abstract: Diffusion models face significant challenges when employed for real world large-scale medical image reconstruction problems such as 3D Computed Tomography (CT)due to the demanding memory, time, and data requirements.Existing works utilizing diffusion priors on single 2D image slice with hand-crafted cross-slice regularization would sacrifice the z-axis consistency, which results in severe artifacts along the z-axis. In this work, we propose a novel framework that enables learning the 3D image prior through position-aware 3D-patch diffusion score blending for reconstructing large-scale 3D medical images. To the best of our knowledge, we are the first to utilize a 3D-patch diffusion prior for 3D medical image reconstruction. Extensive experiments on sparse view and limited angle CT reconstructionshow that our DiffusionBlend method significantly outperforms previous methodsand achieves state-of-the-art performanceon real-world CT reconstruction problems with high-dimensional 3D image (i.e., $256 \times 256 \times 500$).