BACKGROUND:The inflammatory indexes of children with Takayasu arteritis (TAK) usually tend to be normal immediately after treatment, therefore, CT angiography (CTA) has become an important method to evaluate the status of TAK and sometime is even more sensitive than laboratory test results. OBJECTIVE:To evaluate image quality improvement in CTA of children diagnosed with TAK using a deep learning image reconstruction (DLIR) in comparison to other image reconstruction algorithms. METHODS:hirty-two TAK patients (9.14±4.51 years old) underwent neck, chest and abdominal CTA using 100 kVp were enrolled. Images were reconstructed at 0.625 mm slice thickness using Filtered Back-Projection (FBP), 50%adaptive statistical iterative reconstruction-V (ASIR-V), 100%ASIR-V and DLIR with high setting (DLIR-H). CT number and standard deviation (SD) of the descending aorta and back muscle were measured and contrast-to-noise ratio (CNR) for aorta was calculated. The vessel visualization, overall image noise and diagnostic confidence were evaluated using a 5-point scale (5, excellent; 3, acceptable) by 2 observers. RESULTS:There was no significant difference in CT number across images reconstructed using different algorithms. Image noise values (in HU) were 31.36±6.01, 24.96±4.69, 18.46±3.91 and 15.58±3.65, and CNR values for aorta were 11.93±2.12, 15.66±2.37, 22.54±3.34 and 24.02±4.55 using FBP, 50%ASIR-V, 100%ASIR-V and DLIR-H, respectively. The 100%ASIR-V and DLIR-H images had similar noise and CNR (all P > 0.05), and both had lower noise and higher CNR than FBP and 50%ASIR-V images (all P < 0.05). The subjective evaluation suggested that all images were diagnostic for large arteries, however, only 50%ASIR-V and DLIR-H met the diagnostic requirement for small arteries (3.03±0.18 and 3.53±0.51). CONCLUSION:DLIR-H improves CTA image quality and diagnostic confidence for TAK patients compared with 50%ASIR-V, and best balances image noise and spatial resolution compared with 100%ASIR-V.

中文翻译：

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深度学习图像重建算法在提高儿童高安动脉炎CT血管造影图像质量中的应用

背景：大动脉炎（TAK）患儿的炎症指标通常在治疗后即刻趋于正常，因此CT血管造影（CTA）已成为评估TAK状态的重要手段，有时甚至比实验室检查结果更为敏感。 . 目的：与其他图像重建算法相比，使用深度学习图像重建 (DLIR) 评估诊断为 TAK 的儿童 CTA 的图像质量改进。方法：纳入32例TAK患者（9.14±4.51岁）接受100 kVp颈、胸、腹CTA检查。使用滤波反投影 (FBP)、50% 自适应统计迭代重建-V (ASIR-V)、100% ASIR-V 和高设置 DLIR (DLIR-H) 以 0.625 mm 切片厚度重建图像。测量降主动脉和背部肌肉的CT数和标准差（SD），计算主动脉的对比噪声比（CNR）。血管可视化、整体图像噪声和诊断置信度由 2 名观察者使用 5 点量表（5，优秀；3，可接受）进行评估。结果：不同算法重建的图像CT数无显着差异。图像噪声值（以 HU 为单位）为 31.36±6.01、24.96±4.69、18.46±3.91 和 15.58±3.65，主动脉的 CNR 值为 11.93±2.12、15.66±2.37、22.54±3.34 和 24.02±4.55，使用 FBP，50%分别为 ASIR-V、100%ASIR-V 和 DLIR-H。100%ASIR-V 和 DLIR-H 图像具有相似的噪声和 CNR（均 P > 0.05），并且均比 FBP 和 50%ASIR-V 图像具有更低的噪声和更高的 CNR（均 P < 0.05）。主观评价表明，所有图像均可诊断大动脉，但只有 50%ASIR-V 和 DLIR-H 符合小动脉的诊断要求（3.03±0.18 和 3.53±0.51）。结论：与 50%ASIR-V 相比，DLIR-H 提高了 TAK 患者的 CTA 图像质量和诊断信心，与 100%ASIR-V 相比，最佳平衡了图像噪声和空间分辨率。