Abstract：
To perform a systematic literature review and meta-analysis of the two most common commercially available deep-learning algorithms for CT.
We used PubMed, Scopus, Embase, and Web of Science to conduct systematic searches for studies assessing the most common commercially available deep-learning CT reconstruction algorithms: True Fidelity (TF) and Advanced intelligent Clear-IQ Engine (AiCE) in the abdomen of human participants since only these two algorithms currently have adequate published data for robust systematic analysis.
Forty-four articles fulfilled inclusion criteria. 32 studies evaluated TF and 12 studies assessed AiCE. DLR algorithms produced images with significantly less noise (22-57.3% less than IR) but preserved a desirable noise texture with increased contrast-to-noise ratios and improved lesion detectability on conventional CT. These improvements with DLR were similarly noted in dual-energy CT which was only assessed for a single vendor. Reported radiation reduction potential was 35.1-78.5%. Nine studies assessed observer performance with the two dedicated liver lesion studies being performed on the same vendor reconstruction (TF). These two studies indicate preserved low contrast liver lesion detection (> 5 mm) at CTDIvol 6.8 mGy (BMI 23.5 kg/m2) to 12.2 mGy (BMI 29 kg/m2). If smaller lesion detection and improved lesion characterization is needed, a CTDIvol of 13.6-34.9 mGy is needed in a normal weight to obese population. Mild signal loss and blurring have been reported at high DLR reconstruction strengths.
Deep learning reconstructions significantly improve image quality in CT of the abdomen. Assessment of other dose levels and clinical indications is needed. Careful choice of radiation dose levels is necessary, particularly for small liver lesion assessment.
We used PubMed, Scopus, Embase, and Web of Science to conduct systematic searches for studies assessing the most common commercially available deep-learning CT reconstruction algorithms: True Fidelity (TF) and Advanced intelligent Clear-IQ Engine (AiCE) in the abdomen of human participants since only these two algorithms currently have adequate published data for robust systematic analysis.
Forty-four articles fulfilled inclusion criteria. 32 studies evaluated TF and 12 studies assessed AiCE. DLR algorithms produced images with significantly less noise (22-57.3% less than IR) but preserved a desirable noise texture with increased contrast-to-noise ratios and improved lesion detectability on conventional CT. These improvements with DLR were similarly noted in dual-energy CT which was only assessed for a single vendor. Reported radiation reduction potential was 35.1-78.5%. Nine studies assessed observer performance with the two dedicated liver lesion studies being performed on the same vendor reconstruction (TF). These two studies indicate preserved low contrast liver lesion detection (> 5 mm) at CTDIvol 6.8 mGy (BMI 23.5 kg/m2) to 12.2 mGy (BMI 29 kg/m2). If smaller lesion detection and improved lesion characterization is needed, a CTDIvol of 13.6-34.9 mGy is needed in a normal weight to obese population. Mild signal loss and blurring have been reported at high DLR reconstruction strengths.
Deep learning reconstructions significantly improve image quality in CT of the abdomen. Assessment of other dose levels and clinical indications is needed. Careful choice of radiation dose levels is necessary, particularly for small liver lesion assessment.
摘要：
目的：对两种最常见的商用CT深度学习算法进行系统的文献综述和荟萃分析。
方法：我们使用PubMed，Scopus,Embase,和WebofScience进行系统搜索，以评估最常见的商用深度学习CT重建算法：在人类参与者的腹部中的TrueFidelity（TF）和Advanced智能Clear-IQEngine（AiCE），因为目前只有这两种算法有足够的已发布数据进行可靠的系统分析。
结果：44篇文章符合纳入标准。32项研究评估了TF，12项研究评估了AiCE。DLR算法产生的图像噪声明显较少（比IR低22-57.3％），但保留了理想的噪声纹理，对比度噪声比增加，并改善了常规CT上的病变可检测性。DLR的这些改进类似地在双能量CT中注意到,其仅针对单个供应商进行评估。报告的辐射还原电位为35.1-78.5%。九项研究评估了观察者的表现，其中两项专门的肝脏病变研究在同一供应商重建（TF）上进行。这两项研究表明，在CTDIvol6.8mGy（BMI23.5kg/m2）至12.2mGy（BMI29kg/m2）时，保留了低对比肝病变检测（>5mm）。如果需要更小的病变检测和改进的病变表征，正常体重的肥胖人群需要13.6-34.9mGy的CTDIvol.已经报道了在高DLR重建强度下的轻度信号损失和模糊。
结论：深度学习重建可显著改善腹部CT图像质量。需要评估其他剂量水平和临床适应症。仔细选择辐射剂量水平是必要的，特别是对于小的肝脏病变评估。
方法：我们使用PubMed，Scopus,Embase,和WebofScience进行系统搜索，以评估最常见的商用深度学习CT重建算法：在人类参与者的腹部中的TrueFidelity（TF）和Advanced智能Clear-IQEngine（AiCE），因为目前只有这两种算法有足够的已发布数据进行可靠的系统分析。
结果：44篇文章符合纳入标准。32项研究评估了TF，12项研究评估了AiCE。DLR算法产生的图像噪声明显较少（比IR低22-57.3％），但保留了理想的噪声纹理，对比度噪声比增加，并改善了常规CT上的病变可检测性。DLR的这些改进类似地在双能量CT中注意到,其仅针对单个供应商进行评估。报告的辐射还原电位为35.1-78.5%。九项研究评估了观察者的表现，其中两项专门的肝脏病变研究在同一供应商重建（TF）上进行。这两项研究表明，在CTDIvol6.8mGy（BMI23.5kg/m2）至12.2mGy（BMI29kg/m2）时，保留了低对比肝病变检测（>5mm）。如果需要更小的病变检测和改进的病变表征，正常体重的肥胖人群需要13.6-34.9mGy的CTDIvol.已经报道了在高DLR重建强度下的轻度信号损失和模糊。
结论：深度学习重建可显著改善腹部CT图像质量。需要评估其他剂量水平和临床适应症。仔细选择辐射剂量水平是必要的，特别是对于小的肝脏病变评估。
