OBJECTIVE:To investigate feasibility of applying deep learning image reconstruction (DLIR) algorithm in a low-kilovolt enhanced scan of the upper abdomen. METHODS:A total of 64 patients (BMI<28) are selected for the enhanced upper abdomen scan and divided evenly into two groups. The tube voltages in Group A are 100kV in arterial phase and 80kV in venous phase, while tube voltages are 120kV during two phases in Group B. Image reconstruction algorithms used in Group A include the filtered back projection (FBP) algorithm, the adaptive statistical iterative reconstruction-Veo (ASIR-V 40% and 80%) algorithm, and the DLIR algorithm (DL-L, DL-M, DL-H). Image reconstruction algorithm used in Group B is ASIR-V40%. The different reconstruction algorithm images are used to measure the common hepatic artery, liver, renal cortex, erector spinae, and subcutaneous adipose in the arterial phase and the average CT value and standard deviation of the portal vein, liver, spleen, erector spinae, and subcutaneous adipose in the portal phase. The signal-to-noise ratio (SNR) is calculated, and the images are also scored subjectively. RESULTS:In Group A, noise in the aorta, liver, portal vein (the portal phase), spleen (the portal phase), renal cortex, retroperitoneal adipose, and muscle is significantly lower in both the DL-H and ASIR-V80% images, and the SNR is significantly higher than those in the remaining groups (P<0.05). The SNR of each tissue and organ in Group B is not significantly different from that in DL-M, DL-L, and ASIR-V40% in Group A (P>0.05). The subjective image quality scores in the DL-H and B groups are higher than those in the other groups, and the FBP group has significantly lower image quality than the remaining groups (P<0.05). CONCLUSION:For upper abdominal low-kilovolt enhanced scan data, the DLIR-H gear yields a more satisfactory image quality than the FBP and ASIR-V.

中文翻译：

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深度学习图像重建算法在上腹部低千伏扫描中应用的初步评价研究

目的：探讨深度学习图像重建（DLIR）算法在上腹部低千伏增强扫描中应用的可行性。方法：选取64例（BMI<28）患者进行上腹部增强扫描，平均分为两组。A组动脉期管电压100kV，静脉期管电压80kV，B组两期管电压120kV。A组图像重建算法包括滤波反投影（FBP）算法、自适应统计迭代算法重建-Veo（ASIR-V 40% 和 80%）算法，以及 DLIR 算法（DL-L、DL-M、DL-H）。B组中使用的图像重建算法是ASIR-V40%。不同的重建算法图像用于测量肝总动脉、肝脏、肾皮质、竖脊肌、动脉期皮下脂肪和门静脉、肝脏、脾脏、竖脊肌和门静脉期皮下脂肪的平均CT值和标准差。计算信噪比 (SNR)，并对图像进行主观评分。结果：在A组中，DL-H和ASIR-V80%的主动脉、肝脏、门静脉（门脉期）、脾脏（门脉期）、肾皮质、腹膜后脂肪和肌肉中的噪声均显着降低。图像，信噪比显着高于其余组（P<0.05）。B组各组织器官SNR与A组DL-M、DL-L、ASIR-V40%相比差异无统计学意义（P>0.05）。DL-H和B组的主观图像质量得分高于其他组，FBP组的图像质量显着低于其余组（P<0.05）。结论：对于上腹部低千伏增强扫描数据，DLIR-H齿轮比FBP和ASIR-V产生更令人满意的图像质量。