SRECT: Machine-Specific Spatial-Resolution Enhancement in Computed Tomography

Computed Tomography (CT) is an advanced imaging technology. To obtain high-resolution (HR) CT images from low-resolution (LR) sinograms, we present a deep-learning (DL) based CT super-resolution (SR) method. The proposed method combines a SR model in the sinogram domain and the iterative framework into a CT SR algorithm. We unrolled the proposed method into a DL network (SRECT-Net) for adaptive estimation of inherent blurring effects causing by the insufficient sampling of LR X-Ray detector. For CT systems, if the scanning protocol is fixed, the system blur effect will remain relatively stable. Inspired by this fact, the proposed methods can be pre-trained with amounts of simulated datasets, effectively fine-tuned with just a single sample, and then obtain a machine-specific SR model. The proposed SRECT was evaluated via SR CT imaging of a Catphan⁷⁰⁰ phantom and a ham, whose performance was compared to the other DL-based CT SR methods. The results show that the proposed SRECT can provide a CT SR reconstruction performance superior to the other state-of-the-art CT SR methods, demonstrating the potential use in improving CT resolution beyond its hardware limit, lowering the requirement of CT hardware, or reducing X-Ray dose during CT imaging.