AI-enhanced CT reconstruction for texture preservation in clinical imaging

BackgroundAI-enhanced CT reconstruction enables strong noise suppression and dose reduction, but aggressive denoising can distort diagnostically relevant texture, lowering reader confidence-the "texture preservation paradox." Clinical evidence linking texture assessment with validation across AI approaches remains fragmented.ObjectiveTo systematically evaluate AI-enhanced CT reconstruction techniques with emphasis on texture preservation and to summarize clinical validation evidence supporting dose optimization.MethodsThis systematic review followed PRISMA guidelines and searched PubMed, IEEE Xplore, Scopus, and Web of Science (January 2020 to November 2025) for peer-reviewed studies evaluating AI-enhanced CT reconstruction with texture-related image quality assessment and clinical validation.ResultsFifteen clinical studies involving 1847 patients were included in the qualitative synthesis. AI-enhanced CT reconstruction showed consistently improved image quality compared with conventional reconstruction, with representative reports of higher PSNR and SSIM, reduced noise, and improved lesion conspicuity. Multiple studies emphasized preservation of diagnostically relevant texture, addressing the limitation that conventional metrics do not fully capture perceptual fidelity. Reader-based validation indicated improved diagnostic confidence, better inter-reader consistency, and maintained diagnostic acceptability at reduced radiation dose.ConclusionsAI-enhanced CT reconstruction shows promising clinical utility for improving image quality and supporting dose optimization while preserving texture characteristics. However, heterogeneity in study design and evaluation metrics warrants cautious interpretation and highlights the need for standardized assessment.