Cascade multi-output PET image super-resolution reconstruction based on multi-channel input

Positron Emission Tomography (PET) images suffer from low spatial resolution, resulting in suboptimal visual effects and the inability to effectively display subtle pathological areas, hindering the early detection of potential issues. To address this problem, we propose a cascade multi-output super-resolution reconstruction method based on multi-channel input. Firstly, we construct a cascade super-resolution model by introducing degradation functions to refine the LR-to- HR mapping range. Through gradual super-resolution reconstruction, we comprehensively consider information loss and deformation issues during the image resolution reduction process, providing more accurate and higher-quality superresolution reconstruction results. Secondly, we introduce high-resolution CT images to accurately restore image texture details by incorporating additional high-frequency detail information and maintain overall image consistency by the integration of extra structural information. Finally, we incorporate region-based super-resolution detection information to adaptively reconstruct different areas of the image, avoiding distortion caused by excessive super-resolution and blurriness resulting from insufficient super-resolution. Experimental results demonstrate that our approach outperforms other methods, with SSIM, PSNR, and RMSE metrics reaching 0.9607, 34.9438, and 0.0201, respectively, achieving state-of-the-art performance. Furthermore, visual experiments demonstrate a significant improvement in the resolution of the reconstructed PET images using the method proposed in this paper. This effectively compensates for the deficiencies in the original images, providing strong support for the early detection of potential issues.