Application of Deep Learning Algorithm in Overweight Patients with Interstitial Lung Disease
QIN Ruiyao1, WANG Yun1, SONG Wei1, ZHAO Ruijie1, SUI Xin1, SONG Lan1, DU Huayang1,
MA Zhuangfei2, MA Shuo1, FU Haihong1, JIN Zhengyu1
1. Department of Radiology, Peking Union Medical College Hospital, Beijing 100730, China;
2. Canon Medical System Corporation (China) Co., Ltd., Beijing 100015, China
Abstract：Objective To explore the application effect of deep learning algorithm in improving the image quality of low-dose CT
(LDCT) scans and high resolution CT (HRCT) scans in patients with overweight interstitial lung disease (ILD). Methods A total of
20 cases of overweight ILD patients were prospectively enrolled in this study, in which 5 patients were connective tissue diseaserelated
ILD, the remaining 15 cases of unknown etiology. All patients underwent HRCT (120 kVp, automatic tube current) followed
by LDCT (120 kVp, 30 mAs). HRCT images were reconstructed with adaptive iterative dose reduction 3-dimensional (AIDR3D).
and LDCT images were reconstructed with advanced intelligence clear-IQ engine (AiCE lung/bone algorithm, mild/standard/
strong). The image noise, streak artifact, image quality, visualization of normal and abdominal features of ILD were evaluated by
two radiologists, and the image noise and image quality were compared between LDCT and HRCT scans groups. Results Patients
with overweight ILD received a 73% reduction in the effective dose of LDCT scans compared with HRCT scans. The image noise
of LDCT (lung-standard/strong, bone-mild/standard/strong) was reduced to 34.5% to 91.7% of the HRCT (P<0.05), and signal to
noise ratio (range: 24.64-66.23 vs.22.75) was about 1.1 to 2.9 times increased (P<0.001). The overall image quality, streak artifact,
and visualization of normal features (fissures, proximal bronchi and vessels, peripheral bronchi and vessels, subpleural
vessels) between all reconstructed LDCT images and HRCT images showed no significant difference (P>0.05). LDCT
(AiCE, lung-strong) was superior to HRCT (AIDR3D) in visualizing ground glass opacity (P=0.002), while other abnormal
structures (reticulation,bronchiolectasis and honeycombing) were similar between reconstructed LDCT and HRCT
images. Conclusion In overweight ILD patients, deep learning reconstruction can be applied to reduce the radiation dose
significantly and keep the image quality.
秦瑞遥1，王沄1，宋伟1，赵瑞杰1，隋昕1，宋兰1，杜华阳1，马壮飞2，马硕1，付海鸿1，金征宇1. 深度学习算法在超重肺间质性病变患者中的应用研究[J]. 中国医疗设备, 2022, 37(8): 93-98.
QIN Ruiyao1, WANG Yun1, SONG Wei1, ZHAO Ruijie1, SUI Xin1, SONG Lan1, DU Huayang1,
MA Zhuangfei2, MA Shuo1, FU Haihong1, JIN Zhengyu1. Application of Deep Learning Algorithm in Overweight Patients with Interstitial Lung Disease. China Medical Devices, 2022, 37(8): 93-98.
