CT重建算法影响放射学复现性。本研究评估了基于深度学习的图像转换对CT重建算法的影响。本研究包括78例肝细胞癌（HCC）患者，接受了包括无造影剂、迟期动脉期（LAP）、门静脉期（PVP）和迟延期（DP）的四相肝CT检查，采用了滤波反投影（FBP）和先进建模迭代重建（ADMIRE）两种重建算法。使用PVP图像训练卷积神经网络（CNN）模型，将图像从FBP转换为ADMIRE，以及从ADMIRE转换为FBP。LAP、PVP和DP图像用于验证和测试。使用半自动分割工具提取了每位患者的放射组学特征。我们使用一致性相关系数（CCC）评估了原始FBP（oFBP）与原始ADMIRE（oADMIRE）、oFBP与转换FBP（cFBP）以及oADMIRE与转换ADMIRE（cADMIRE）之间的放射组学复现性。在包括30名患者的测试组中，oFBP与oADMIRE的CCC值为0.65，LAP的可再现特征比例（CCC ≥ 0.85）为32.9%（524/1595），PVP为35.9%（573/1595），DP为43.8%（699/1595）。oFBP与cFBP的值增至0.92，LAP的可再现特征比例为83.9%（1339/1595），PVP为71.0%（1133/1595），DP为79.7%（1271/1595）。同样，oADMIRE与cADMIRE的值增至0.87，LAP的可再现特征比例为68.1%（1086/1595），PVP为82.1%（1309/1595），DP为76.2%（1216/1595）。基于CNN的CT重建算法图像转换提高了HCC的放射组学复现性。本研究表明，使用基于CNN的图像转换技术显著提高了HCC患者的放射组学特征的复现性，凸显了该技术在HCC患者放射组学研究中的潜力。通过CNN技术基于CT重建算法之间的图像转换，在HCC的放射组学特征复现性方面取得了改进。这是第一项在HCC患者中证明各种放射组学特征改进的临床研究。本研究促进了不同CT重建算法在放射组学研究中的重现性和泛化能力。© 2023. 作者（们）和欧洲放射学学会许可的专有许可证下发表。

CT reconstruction algorithms affect radiomics reproducibility. In this study, we evaluate the effect of deep learning-based image conversion on CT reconstruction algorithms.This study included 78 hepatocellular carcinoma (HCC) patients who underwent four-phase liver CTs comprising non-contrast, late arterial (LAP), portal venous (PVP), and delayed phase (DP), reconstructed using both filtered back projection (FBP) and advanced modeled iterative reconstruction (ADMIRE). PVP images were used to train a convolutional neural network (CNN) model to convert images from FBP to ADMIRE and vice versa. LAP, PVP, and DP images were used for validation and testing. Radiomic features were extracted for each patient with a semi-automatic segmentation tool. We used concordance correlation coefficients (CCCs) to evaluate the radiomics reproducibility for original FBP (oFBP) vs. original ADMIRE (oADMIRE), oFBP vs. converted FBP (cFBP), and oADMIRE vs. converted ADMIRE (cADMIRE).In the test group including 30 patients, the CCC and proportion of reproducible features (CCC ≥ 0.85) for oFBP vs. oADMIRE were 0.65 and 32.9% (524/1595) for LAP, 0.65 and 35.9% (573/1595) for PVP, and 0.69 and 43.8% (699/1595) for DP. For oFBP vs. cFBP, the values increased to 0.92 and 83.9% (1339/1595) for LAP, 0.89 and 71.0% (1133/1595) for PVP, and 0.90 and 79.7% (1271/1595) for DP. Similarly, for oADMIRE vs. cADMIRE, the values increased to 0.87 and 68.1% (1086/1595) for LAP, 0.91 and 82.1% (1309/1595) for PVP, and 0.89 and 76.2% (1216/1595) for DP.CNN-based image conversion between CT reconstruction algorithms improved the radiomics reproducibility of HCCs.This study demonstrates that using a CNN-based image conversion technique significantly improves the reproducibility of radiomic features in HCCs, highlighting its potential for enhancing radiomics research in HCC patients.Radiomics reproducibility of HCC was improved via CNN-based image conversion between two different CT reconstruction algorithms. This is the first clinical study to demonstrate improvements across a range of radiomic features in HCC patients. This study promotes the reproducibility and generalizability of different CT reconstruction algorithms in radiomics research.© 2023. The Author(s), under exclusive licence to European Society of Radiology.