  Diffusion models face significant challenges when employed for large-scale
medical image reconstruction in real practice such as 3D Computed Tomography
(CT). Due to the demanding memory, time, and data requirements, it is difficult
to train a diffusion model directly on the entire volume of high-dimensional
data to obtain an efficient 3D diffusion prior. 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 reconstruction show that our DiffusionBlend method significantly
outperforms previous methods and achieves state-of-the-art performance on
real-world CT reconstruction problems with high-dimensional 3D image (i.e.,
$256 \times 256 \times 500$). Our algorithm also comes with better or
comparable computational efficiency than previous state-of-the-art methods.
