Rethinking Real-World Thick-Slice CT Super-Resolution via Thickness-Aware Degradation Modeling

Thick-slice CT super-resolution (SR) aims to recover high-resolution (HR) thin-slice volumes from low-resolution (LR) thick-slice scans. Existing methods primarily strengthen reconstruction priors, but commonly use geometric resampling (e.g., nearest-neighbor or linear interpolation) to model the HR-to-LR degradation. As point-wise estimators, these geometric operators determine degradation weights only from slice-center locations while ignoring physical slice thickness, thereby failing to reproduce thickness-dependent partial-volume effects and limiting the reliability of SR models in real-world settings. We formulate a slice-thickness model (STM), a slice sensitivity profile (SSP)-parameterized linear operator that explicitly separates effective slice thickness from slice spacing. The full width at half maximum (FWHM) of the SSP specifies the effective thickness, whereas the sampling locations determine the LR slice centers. STM supports both analytical and data-fitted SSPs and can be used consistently for end-to-end supervised and unsupervised iterative SR. Experiments across real-paired CT datasets and multiple SR paradigms show that STM improves degradation fidelity, SR quality, and downstream anatomical segmentation. It further converts HR-only CT volumes into scalable synthetic supervision that complements limited real pairs. These results establish physically grounded degradation modeling as a key component of real-world thick-slice CT SR.