超低剂量平扫CT深度学习图像重建评价肺部病灶的可行性  

Feasibility of ultra-low-dose noncontrast CT based on deep learning image reconstruction to evaluate chest lesions

作　　者：赵珂珂 蒋蓓蓓 张璐 王凌云 张亚平 解学乾[1] ZHAO Keke;JIANG Beibei;ZHANG Lu;WANG Lingyun;ZHANG Yaping;XIE Xueqian(Department of Radiology,Shanghai General Hospital,Shanghai Jiao Tong University School of Medicine,Shanghai 200080,China)

机构地区：[1]上海交通大学医学院附属第一人民医院放射科,上海200080

出　　处：《上海交通大学学报（医学版）》2022年第8期1062-1069,共8页Journal of Shanghai Jiao tong University:Medical Science

基　　金：国家自然科学基金面上项目(81971612);科技部国际合作项目(2016YFE0103000);上海市教育委员会高峰高原学科建设计划(20181814)。

摘　　要：目的·探讨使用超低剂量CT (ultra-low-dose CT, ULDCT)平扫评价基于实体瘤疗效评价标准(response evaluation criteria in solid tumors,RECIST)定义的肺部靶病灶和磨玻璃结节的可行性。方法·2020年4月-6月纳入接受了胸部ULDCT平扫(0.07~0.14 mSv)和低剂量增强CT检查(2.38 mSv),而且有RECIST标准定义的可测量肺部靶病灶或直径≤1 cm磨玻璃结节的患者。每例患者均重建了4组图像,包括3组ULDCT图像,分别为80%强度的多模型自适应统计迭代重建(adaptive statistical iterative reconstruction-V with an 80%strength level,ASIR-V-80%)图像、中等强度的深度学习重建(deep learning image reconstruction of moderate strength,DLIR-M)图像和高强度的深度学习重建(deep learning image reconstruction of high strength,DLIR-H)图像,以及1组作为参考标准的增强CT图像。结果·80例患者符合入组标准,平均年龄(62±11)岁,共80个靶病灶和27个磨玻璃结节。3组ULDCT图像的肺部靶病灶测量值(r分别为0.988、0.987和0.990)、≤1 cm磨玻璃结节的直径测量值(r分别为0.905、0.906和0.969)、非肺门淋巴结靶病灶测量值(r分别为0.969、0.957和0.977)、肺门淋巴结靶病灶测量值(r分别为0.972、0.994和0.994)与增强CT有很高的相关性。Bland-Altman分析显示,DLIR-H重建图像中肺部靶病灶测量值的大小与参考值的差异为4.3%(95%一致性界限:-5.7%~14.3%),非肺门淋巴结靶病灶测量值的大小与参考值的差异为5.1%(-9.1%~19.3%),优于ASIR-V-80%[8.5%(-3.3%~20.3%),9.7%(-6.0%~25.3%)]和DLIR-M [8.5%(-4.2%~21.3%,8.8%(-9.9%~27.5%)]。DLIR-H重建图像中的肺门淋巴结病灶测量值大小与参考值的差异为18.3%(8.8%~27.9%),优于ASIR-V-80%[20.2%(-1.2%~41.5%)]和DLIR-M [23.4%(13.5%~33.2%)]。DLIR-H重建图像中磨玻璃结节测量值与参考值的差异为7.0%(-5.7%~19.7%),优于ASIR-V-80%[14.4%(-4.4%~33.2%)]和DLIR-M [16.3%(-4.1%~36.7%)]。结论·基于DLIR-H重建的ULDCT平扫图像中的肺部靶病灶和直径≤Objective·To explore the feasibility of using noncontrast ultra-low-dose CT(ULDCT) to evaluate chest target lesions based on response evaluation criteria in solid tumors(RECIST) and ground glass nodules(GGNs). Methods·From April to June2020, patients who underwent noncontrast chest ULDCT(0.07-0.14 mSv) and low-dose enhanced CT(2.38 mSv) and had measurable target lesions defined by RECIST and GGNs ≤1 cm in diameter were included. Four sets of CT images were reconstructed for each patient, including 3 sets of ULDCT images, i.e., adaptive statistical iterative reconstruction-V with an 80%strength level(ASIR-V-80%), deep learning image reconstruction of moderate strength(DLIR-M) and deep learning image reconstruction of high strength(DLIR-H), and one set of enhanced CT images as the reference. Results·Eighty patients who had 80target lesions and 27 GGNs met the inclusion criteria, and the average age was(62±11) years old. Between the ULDCT images(3sets of image reconstruction) and enhanced CT, the measured values of target lesions(r=0.988, 0.987 and 0.990, respectively), GGNs≤1 cm in diameter(r=0.905, 0.906 and 0.969, respectively), mediastinal lymph node target lesions(r=0.969, 0.957 and 0.977,respectively), and hilar lymph node target lesions(r=0.972, 0.994 and 0.994, respectively) were highly correlated. Bland-Altman analysis showed that the difference between the measured size of lung target lesion and reference value in DLIR-H reconstruction image was 4.3%(95% limits of agreement:-5.7%-14.3%), and the difference between the measured size of mediastinal lymph node target lesion and reference value was 5.1%(-9.1%-19.3%), which was better than that of ASIR-V-80% [8.5%(-3.3%-20.3%),9.7%(-6.0%-25.3%)] and DLIR-M [8.5%(-4.2%-21.3%), 8.8%(-9.9%-27.5%)]. The difference between the measured size of lymph node target lesions in DLIR-H images and the reference value was 18.3%(8.8%-27.9%), better than ASIR-V-80% [20.2%(-1.2%-41.5%)] and DLIR-M [23.4%(13.5%-33.2%)]. The difference between the measured size of GGNs in

关 键 词：肺癌 RECIST 深度学习 超低剂量CT 

分 类 号：R445.4[医药卫生—影像医学与核医学]