He Weitao, Xu Ping, Zhang Mengchen, Xu Rulin, Shen Xiaodi, Mao Ren, Li Xue-Hua, Sun Can-Hui, Zhang Ruo-Nan, Lin Shaochun
First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Research Collaboration, Canon Medical Systems, Guangzhou, Guangdong, China.
Abdom Radiol (NY). 2024 Sep 21. doi: 10.1007/s00261-024-04590-4.
Lifelong re-examination of CT enterography (CTE) in patients with inflammatory bowel disease (IBD) may be necessary, and reducing radiation exposure during CT examinations is crucial. We investigated the potential application of deep learning reconstruction (DLR) in CTE to reduce radiation dose and improve image quality in IBD.
Thirty-six patients with known or suspected IBD were prospectively recruited to the low-dose CTE (LDCTE) group, while forty patients were retrospectively selected from previous clinical standard-dose CTE (STDCTE) scans as controls. STDCTE images were reconstructed with hybrid-IR (adaptive iterative dose reduction 3-dimensional [AIDR3D], standard setting); LDCTE images were reconstructed with AIDR3D and DLR (Advanced Intelligence ClearIQ Engine [AiCE], Body mild/standard/strong, Sharp Body mild/standard/strong setting). The effective radiation dose (ED), image noise, signal-to-noise ratio (SNR), overall image quality, subjective image noise, and diagnostic effectiveness were compared between the LDCTE and STDCTE groups.
Compared with STDCTE, the ED of LDCTE was lower by 54.1% (p<0.001). Compared with STDCTE-AIDR3D, LDCTE-AIDR3D reconstruction objective image noise and SNR were greater (p<0.05), the subjective overall image quality was lower (p<0.05), and the diagnostic efficiency was lower (AUC=0.52, p<0.05). The SNRs of reconstructedimages of LDCTE-AiCE Body Strong and LDCTE-AiCE Body Sharp standard/strong groups were greater than that of STDCTE-AIDR3D group (all p<0.05), and the diagnostic performance was better than or comparable to that of STDCTE; the AUCs were 0.83, 0.76 and 0.76, respectively CONCLUSION: Compared with STDCTE with AIDR3D, LDCTE with DLR effectively reduced the radiation dose and improve image quality in IBD patients.
对炎症性肠病（IBD）患者进行终身CT小肠造影（CTE）复查可能是必要的，而在CT检查期间减少辐射暴露至关重要。我们研究了深度学习重建（DLR）在CTE中用于减少IBD患者辐射剂量并改善图像质量的潜在应用。
前瞻性招募36例已知或疑似IBD的患者进入低剂量CTE（LDCTE）组，同时从既往临床标准剂量CTE（STDCTE）扫描中回顾性选取40例患者作为对照。STDCTE图像采用混合迭代重建（自适应迭代剂量降低三维[AIDR3D]，标准设置）重建；LDCTE图像采用AIDR3D和DLR（高级智能清晰图像引擎[AiCE]，身体轻度/标准/强化，锐利身体轻度/标准/强化设置）重建。比较LDCTE组和STDCTE组的有效辐射剂量（ED）、图像噪声、信噪比（SNR）、整体图像质量、主观图像噪声和诊断效能。
与STDCTE相比，LDCTE的ED降低了54.1%（p<0.001）。与STDCTE-AIDR3D相比，LDCTE-AIDR3D重建的客观图像噪声和SNR更高（p<0.05），主观整体图像质量更低（p<0.05），诊断效率更低（AUC=0.52，p<0.05）。LDCTE-AiCE身体强化组和LDCTE-AiCE锐利身体标准/强化组重建图像的SNR高于STDCTE-AIDR3D组（均p<0.05），诊断性能优于或与STDCTE相当；AUC分别为0.83、0.76和0.76。结论：与采用AIDR3D的STDCTE相比，采用DLR的LDCTE有效降低了IBD患者的辐射剂量并改善了图像质量。
