Objective To evaluate the impact of deep learning reconstruction algorithm on the image quality of head and neck CT angiography (CTA) at 100 kVp. Methods CT scanning was performed at 100 kVp for the 37 patients who underwent head and neck CTA in PUMC Hospital from March to April in 2021.Four sets of images were reconstructed by three-dimensional adaptive iterative dose reduction (AIDR 3D) and advanced intelligent Clear-IQ engine (AiCE) (low,medium,and high intensity algorithms),respectively.The average CT value,standard deviation (SD),signal-to-noise ratio (SNR),and contrast-to-noise ratio (CNR) of the region of interest in the transverse section image were calculated.Furthermore,the four sets of sagittal maximum intensity projection images of the anterior cerebral artery were scored (1 point:poor,5 points:excellent). Results The SNR and CNR showed differences in the images reconstructed by AiCE (low,medium,and high intensity) and AIDR 3D (all P<0.01).The quality scores of the image reconstructed by AiCE (low,medium,and high intensity) and AIDR 3D were 4.78±0.41,4.92±0.27,4.97±0.16,and 3.92±0.27,respectively,which showed statistically significant differences (all P<0.001). Conclusion AiCE outperformed AIDR 3D in reconstructing the images of head and neck CTA at 100 kVp,being capable of improving image quality and applicable in clinical examinations.
目的 评价100 kVp条件下深度学习重建算法对头颈CT血管造影(CTA)图像质量的影响。方法 2021年3至4月北京协和医院行头颈CTA检查的37例患者,采用100 kVp管电压进行CT扫描,基于三维自适应迭代剂量降低(AIDR 3D)和深度学习高级智能Clear-IQ引擎(AiCE)低、中、高强度算法重建出4组图像,测量并计算横断位图像感兴趣区的平均CT值、标准差(SD)、信噪比(SNR)和对比噪声比(CNR)等客观指标,并分别对4组大脑前动脉矢状位最大密度投影图像进行主观评分(1分:差,5分:优秀)。结果 AiCE低、中、高强度和AIDR 3D组图像的SNR和CNR值比较差异均有统计学意义(P均<0.01)。AiCE低、中、高强度和AIDR 3D组图像的质量评分分别为(4.78±0.41)、(4.92±0.27)、(4.97±0.16)、(3.92±0.27)分,3组AiCE图像与AIDR 3D组比较差异均有统计学意义(P均<0.001)。结论 在头颈CTA检查图像质量方面,100 kVp条件下深度学习AiCE重建算法优于AIDR 3D,能够显著提高图像质量,可以在临床检查中加以应用。.
Keywords: CT angiography; deep learning reconstruction; image quality; three-dimensional adaptive iterative dose reduction.