Mixed Prior Diffusion Posterior Sampling for High Resolution CT Reconstruction

Diffusion models have a demonstrated ability to estimate complex prior distributions, including classes of image data. Diffusion Posterior Sampling (DPS) has recently emerged as a powerful framework for incorporating such learned priors into inverse problems; however, the performance of DPS is limited by the ability to train high-quality priors. This is particularly true in x-ray computed tomography, where patient data must balance noise and spatial resolution within acceptable x-ray dose limits. In this work, we propose Mixed Prior Diffusion Posterior Sampling (Mixed Prior DPS) that integrates global and local diffusion priors in the reconstruction. The global diffusion model is trained on full CT volume with modest spatial resolution to capture large-scale anatomical structures and lower frequency content. A local diffusion model trained on high-resolution patches focuses on fine-scale, high-frequency details. The two priors are combined in the Fourier domain to form a mixed posterior mean, and a masking strategy is employed to apply prior mixing selectively within an appropriate local region-of-interest while employing standard-resolution DPS elsewhere. This framework permits the use of modest resolution CT datasets for global context in conjunction with specialized high-resolution datasets (e.g. organ or tissue specific scans based on specialized protocols or on ex vivo samples where high-resolution, low-noise images can be collected without radiation dose constraints). In this work, we created a high-resolution lung training dataset for the local prior model using a CBCT benchtop system and a (plastinated) physical lung specimen. Experimental results on simulated low-dose CT demonstrate that the proposed method improves spatial resolution and data consistency as compared with filtered backprojection, standard DPS, and DPS with resampling, while preserving fine lung structures. These results highlight the effectiveness of multi-resolution prior integration for low-dose CT reconstruction.