Research on CT image deblurring method based on focal spot intensity distribution

Abstract

The finite focal spot of the X-ray source is a fundamental physical bottleneck limiting the spatial resolution of Computed Tomography (CT), as its penumbra blurring severely degrades image detail discernibility. To overcome this limitation, this paper proposes a physics-informed deblurring method. Firstly, to circumvent the challenge of acquiring ideal reference images in real scenarios, we developed a high-fidelity physical forward model to generate a high-quality paired dataset with the help of precise measurement of the focal spot’s 2D intensity distribution by the circular hole edge response backprojection method. Secondly, to learn the inverse mapping from blurred projections to ideal projections, we designed an Enhanced Phase U-Net (EPU-Net) deep learning network, which contains an innovative Eulerian Phase Unit (EPU) module. This module transforms feature maps into the Fourier domain, leveraging the high-fidelity structural information carried by the phase spectrum. Through a phase-attention-driven mechanism, it guides and rectifies the amplitude spectrum information corrupted during blurring. This mechanism enables the network to accurately restore the high-frequency components crucial to image details. Both simulated and physical experiments illustrate that EPU-Net outperforms state-of-the-art algorithms such as RCAN and CMU-Net in terms of Peak Signal-to-Noise Ratio and Feature Similarity. More importantly, in visual quality assessments, EPU-Net successfully restored fine structures indistinguishable by other methods, demonstrating exceptional deblurring performance and robust generalization capability. This study presents a novel approach combining physics-model-driven data generation and deep network-based inverse solution learning to enhance image quality in high-resolution CT systems.