Foveated-Imaging Geometry CT Architecture and Seeded Diffusion Model Enabling Global Super-Resolution Reconstruction

Abstract

For X-ray computed tomography (CT), a smaller detector pixel size generally leads to higher scanner spatial resolution, but inevitably increases system cost as well as data overhead in acquisition and processing. To achieve high-resolution (HR) CT imaging in a more resource-efficient manner, we propose a Foveated-Imaging Geometry CT (FIGCT) architecture, which integrates local HR data into an acquisition scheme dominated by low-resolution (LR) measurements. We further develop a Diffusion Probabilistic FIGCT Super-Resolution Reconstruction (DPFSR) framework to generate global HR CT images over the full field of view (FOV). The concept of FIGCT is first established, and its typical configurations are characterized according to the arrangement of HR data. Two key indices, namely the HR data fraction (HDF) and the LR-to-HR detector pixel size ratio (LHR), are introduced to describe the FIGCT geometry. The proposed DPFSR incorporates local HR information into intermediate clean-image estimates in both the projection and image domains during the reverse diffusion process. This additional step not only guides HR image generation from LR data, but also improves data consistency between the clean-image estimates and the originally measured data. Preliminary numerical simulation results on FIGCT show that the proposed architecture provides high-precision CT images within the region of interest (ROI) corresponding to the HR data, while the spatial resolution deteriorates rapidly outside the ROI. With DPFSR, global HR reconstruction is achieved on the AAPM Grand Challenge dataset and swine lung CT data, outperforming existing SR methods in terms of Learned Perceptual Image Patch Similarity (LPIPS), PSNR, and SSIM.