Super-Resolution in Low Dose X-ray CT via Focal Spot Mitigation With Generative Diffusion Networks

Abstract

Advancing the resolution capabilities of X-ray CT imaging, particularly in low-dose applications, is a paramount pursuit in the field. This quest for superior spatial detail is hindered by the pervasive issue of focal spot blooming, which plagues medical scanners due to the finite nature of the emittance surface in X-ray sources. Such a phenomenon introduces optical distortions in the measurements that limit the achievable resolution. In response to this challenge, we introduce a novel approach: Focal Spot Diffusion CT (FSD-CT). Unlike traditional methods that rely on limited and simplified idealizations of X-ray models, FSD-CT adopts a more complex, realistic representation of X-ray sources. FSD-CT leverages a generative diffusion-based reconstruction framework, guided by a forward imaging model for sample consistency and a frequency selection module for enhanced spectral content. FSD-CT successfully mitigates focal spot blooming without imposing a significant computational burden when compared to other diffusion-based reconstruction methods, offering a versatile solution for improving CT resolution. Computational experiments using simulations based on commercial medical scanners show FSD-CT delivers gains of up to 4 dB in fan-beam tomography compared to benchmarks such as filtered backprojection, end-to-end CNNs, and state-of-the-art diffusion models. The technique's robustness is confirmed in challenging scenarios, including sparse angle CT, off-distribution samples, and reconstructions from real projections. FSD-CT helps to overcome limitations in spatial resolution and offers a plausible solution that could be applied in clinical CT imaging after more in-depth studies are conducted.