Advances in Research on Low-dose CT Imaging Algorithm Based on Deep Learning
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摘要:
计算机断层扫描成像（CT）技术具有成像速度快分辨率高的优点，广泛应用于医学临床诊断中。然而，提高剂量辐射会引发人体组织器官受损，降低剂量又会造成成像质量严重下降。为解决上述矛盾，在确保成像质量满足临床诊断需求的条件下，研究如何最大程度地降低X射线辐射对人体造成的伤害，已成为低剂量CT成像技术的研究热点。近年来，在人工智能领域深度学习方法快速发展，已广泛应用于图像处理、模式识别、信号处理等领域。与此同时，大数据驱动下的深度学习方法在LDCT成像领域的应用也有了长足的发展。本文从CT成像的过程、低剂量CT噪声建模以及成像算法的设计3方面，介绍近年来国内外低剂量CT成像算法的发展，尤其对深度学习领域的成像算法进行阐述与分析，并对LDCT图像成像领域未来的发展进行展望。
Abstract:Computed tomography (CT) is widely used in clinical diagnosis because of its fast imaging speed and high resolution. However, higher doses of radiation will cause damages to human tissues and organs, while lower doses will lead to serious deterioration of imaging quality. In order to solve the above contradiction, researchers have focused on the low-dose CT imaging technology to study how to reduce the harm caused by radiation to the human body to the greatest extent under the condition of ensuring the imaging quality to meet the needs of clinical diagnosis. In recent years, deep learning has developed rapidly in the field of artificial intelligence, and has been widely used in image processing, pattern recognition, signal processing fields. Driven by big data, LDCT imaging algorithms based on deep learning have made great progress. This paper studies the development of low-dose CT imaging algorithms in recent years in terms of three aspects: the process of CT imaging, the noise modeling of low-dose CT, and the design of imaging algorithms. In particular, the imaging algorithms in the field of deep learning are systematically elaborated and analyzed. Finally, future developments in the field of LDCT image artifact suppression are also prospected.
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Keywords:
- deep learning /
- low dose CT /
- artifact suppression /
- noise modeling
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目前中国慢性阻塞性肺疾病（chronic obstructive pulmonary disease ，COPD）发病率较高，且进展迅速。根据WHO及王辰院士对COPD研究结果显示[1−2]，近几年中国人群中约有1亿左右人口患有COPD，较10年前增加了约70%，同时预计全球COPD患病率将在未来40年继续上升，到2060年，每年可能有超过540万人死于COPD和相关疾病。中国大多数患者对COPD了解较少、肺功能检查的重要性及普及率偏低、诊断及治疗及时性差等原因导致患者因咳嗽、气短等临床症状就诊时已达到轻度COPD甚至重度COPD的诊断标准。目前临床诊断COPD主要依据为肺功能检查（pulmonary function test，PFT），且其为金标准，但此检查方法存在诸多缺陷[3−4]，如对患者配合度要求高、肺代偿能力高等。可能因为多种因素导致疾病的诊断及轻、重度分级出现误差，使临床医师对疾病的个体化治疗方案出现错误，进而影响患者疾病的及时诊断，从而形成加重患者的疾病严重程度、经济负担及精神压力等不利结果。研究表明[5]部分患者CT体素评估肺功能严重程度相较于PFT具有较大的优势。另有学者[6]通过定量CT体素对COPD相关指标的研究得出此方法提供了COPD每种疾病成分的全面肺功能信息，可作为肺功能的成像生物标志物，进而指出基于体素定量CT测量COPD的准确性相对较高。因此，本研究拟通过基于体素的CT定量指标，分析COPD严重程度加重的危险因素，建立个体化预测重度COPD的列线图模型。
1. 资料与方法
1.1 一般资料
回顾性收集自2020年5月至2021年9月在延安大学附属医院行双气相扫描及肺功能检查确诊的COPD患者，纳入标准：①符合2023年COPD诊疗指南解读[7]中的诊断标准；②肺功能检查结果准确、完整；③吸气及呼气双相图像完整。排除标准为：①呼吸配合欠佳，图像质量差；②肺内影响定量分析的病变，如肺癌、大片状实变或感染及肺不张等；③有肺部手术史，如肺切除、冠脉介入术后；④其他呼吸系统疾病如哮喘、肺结核、支气管扩张等；⑤心、肝、肾功能不全患者。最终符合条件的COPD患者118例。根据GOLD 2023 COPD报告[7]将患者分为轻度COPD组(GOLDⅠ、Ⅱ级)：66例；重度COPD组(GOLD Ⅲ、Ⅳ级)：52例。所有患者均签署知情同意书。
1.2 CT吸气相扫描、图像后处理及定量测量
CT扫描前告知患者扫描目的、方法及注意事项，训练患者深吸气及深呼气后屏气。采用UCT-760 128层螺旋CT（上海联影）进行全肺扫描，扫描参数：管电压120 KV，自适应动态管电流范围约30~40 mAs,符合国际CT低剂量指标[8]，机架旋转一周的时间为0.5 s，螺距为0.5，矩阵为512×512。受检者取仰卧位，双手抱头，头先进。从肺尖至肺底行全肺扫描。双气相图像重建层厚为1 mm,重建间隔为0.625 mm，重建算法为骨算法。
所得图像以Dicom格式导入“数字肺”测试平台（Dexin-FACT“数字肺”工作站，陕西西安）进行图像配准，“数字肺”平台为量化分析COPD的软件。采用基于体素定量检测方法[9−11]，吸气相CT值 > −950 HU，呼气相 CT 值≤−856 HU 的像素占全肺容积的百分比（the percentage of the area of functional small airway disease，fSAD%）为小气道病变区，标记为黄色区；吸气相≤−950 HU，呼气相≤−856 HU 的像素占全肺容积的百分比（the percentage of the area ofemphysema，Emph%）为肺气肿区，标记为红色区；吸气相 > −950 HU，呼气相 > −856 HU 的像素占全肺容积的百分比为正常区，标记为绿色区。（图1 a ~ c）。
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图 1 双气相配准图注：图1a~c.男59岁，COPD患者，GOLD II级，双气相配准流程图。图a.吸气相CT；图b.呼气相CT图；图c.呼气相与吸气相配准图。 黄色区为小气道病变区，红色区代表肺气肿区，绿色为正常区。Figure 1. Two-gas phase registration diagram1.3 肺功能
取坐位测定肺功能，测量参数包括第1秒用力呼气容积（FEV1）的实测值与预计值的比值（FEV1%pred）、FEV1与用力肺活量(FVC)的比值（FEV1/FVC）等指标。
1.4 统计学分析
采用SPSS 20.0和R软件进行数据统计，采用R软件、GraphPad prism 9.2.0 进行图像绘制。计量资料用
$ \bar x \pm s$ 表示，若符合正态分布的计量资料，使用独立样本t检验比较轻重度COPD组间的差异。将差异有统计学意义的指标纳入多因素logistic回归分析，确定重度COPD组的的独立危险因素。在R软件构建预测重度COPD组的诺模图。采用受试者工作特征曲线（receiver operating characteristic curve，ROC）曲线下面积（area under the curve，AUC），Bootstrap 重复抽样绘制校准曲线，以及Hosmer-Lemeshow拟合优度检验等方法以评估诺模图的预测效能。以P＜0.05为差异有统计学意义。2. 结果
2.1 一般资料及基于体素CT定量参数的比较
两组间年龄及体重指数无统计学意义（P＞0.05），两组间吸烟指数、肺功能指标（FVC、FEV1%、FEV1/FVC%）及基于体素的双气相配准CT定量指标（Normal%、Emph%、fSAD%）均有统计学意义（均P＜0.05），其中，吸烟指数重度COPD组明显高于轻度COPD组；Emph%、fSAD%重度COPD组高于轻度COPD组，Normal%重度COPD组低于轻度COPD组。（表1）
表 1 一般情况Table 1. General situation变量 轻度COPD组 重度COPD组 t/Z P 年龄 64.95±8.54 63.02±9.61 1.155 0.25 BMI 23.39±3.23 21.99±3.14 1.719 0.091 吸烟指数 619.77±629.31 1202.13 ±849.72−4.138 0.000 FVC 2.96±0.90 2.05±0.55 6.574 0.000 FEV1%pred 66.93±17.91 32.4±8.10 13.953 0.000 FEV1/FVC% 60.73±12.47 47.03±15.01 5.411 0.000 Normal(%) 37.71±15.13 28.71±14.24 −3.499 0.000 Empha(%) 17.75±9.83 24.49±12.31 −2.959 0.003 fSAD(%) 30.73±9.96 35.08±6.83 −2.802 0.006 注：Normal(%)为正常肺组织占全肺体积的百分比；fSAD%为小气道病变占全肺体积的百分比；Emph%为肺气肿病变占全肺体积的百分比。 2.2 多因素Logistic回归分析
通过多因素Logistic回归分析发现，吸烟指数、fSAD%、Emph%及Normal%是重度COPD的独立风险因素。（表2、图2）
表 2 多因素logistic回归分析结果Table 2. Results of multivariate logistic regression analysis变量 B SE Wals P OR 95%CI 年龄 0.026 0.036 0.514 0.473 1.026 0.957~1.100 BMI −0.086 0.097 0.795 0.373 0.917 0.759~1.109 吸烟指数 0.001 0.001 4.207 0.04 1.001 1.000~1.002 Normal(%) 0.305 0.139 4.774 0.029 1.356 1.032~1.783 Empha(%) 0.394 0.17 5.336 0.021 1.482 1.061~2.070 fSAD(%) 0.226 0.105 4.665 0.031 1.253 1.021~1.538 注：Normal(%)为正常肺组织占全肺体积的百分比；fSAD%为小气道病变占全肺体积的百分比；Emph%为肺气肿病变占全肺体积的百分比。B为各自变量不同分类水平在模型中的系数；SE为标准误差；Wals为检验每个自变量的系数是否显著；P为统计值；OR为优势比；95%cl为95%置信区间。 2.3 预测重度COPD列线图的建立
本研究基于上述4项危险因素，建立预测重度COPD的风险模型。根据本研究绘制的列线图中各变量对应的分值，影响患者死亡风险权重从高至低的因素依次为:Normal%、Emph%、fSAD%和吸烟指数（图3）。
2.4 Nomogram的校正曲线
校准图显示，模型校准曲线与标准曲线接近，提示模型的校准能力好，反映模型预测重度COPD风险与实际风险一致程度高。（图3）
2.5 重度COPD组危险因素及logistic模型ROC预测价值
将各危险因素CT定量指标行ROC曲线分析，得出Logistic regression model、吸烟指数、fSAD%、Emph%及Normal%可以鉴别轻度及重度COPD（P＜0.01）（表3，图4）。曲线下面积由高到低依次为：吸烟指数、Normal%、fSAD%、Emph%，具体截断值、灵敏度、特异度及95%可信区间见表3。
表 3 重度COPD组危险因素、logistic模型的ROC预测价值Table 3. Risk factors in the severe COPD group and the predictive values of logistic models for ROC变量 截断值 AUC 95%CI 敏感度 特异度 P 吸烟指数 0.379 0.722 0.631~0.813 0.712 0.667 0.000 Normal% 0.365 0.688 0.590~0.786 0.788 0.577 0.000 Empha% 0.352 0.659 0.557~0.761 0.519 0.833 0.003 fSAD% 0.343 0.669 0.571~0.766 0.904 0.439 0.002 Logistic regression model 0.461 0.786 0.704~0.867 0.673 0.788 0.000 注：Normal(%)为正常肺组织占全肺体积的百分比；fSAD%为小气道病变占全肺体积的百分比；Emph%为肺气肿病变占全肺体积的百分比。 ![]()
图 4 筛选出的变量预测患者重度COPD风险的ROC曲线Figure 4. ROC curves of the screened variables predicting the risk of severe COPD in patients3. 讨论
最新版慢性阻塞性肺疾病全球倡议（global initiative for chronic obstructive lung disease，GOLD）2021版于2020年11月发布，其对COPD定义无明显变化，即以持续的呼吸道症状和气流受限为特征、具有病因复杂、危险因素较多及相互影响、病因分析困难性等特点、可预防及治疗的疾病[12−13]。COPD为最常见的气道疾病，同时也是中国在近10年国家计划中重点防治的疾病。基于中国实际情况及国内外研究进展，肺功能虽为诊断的最佳依据，但其存在较多缺陷，如对患者配合度要求高、肺代偿能力高对轻度COPD诊断效能偏低、大多数病变位于肺上叶即使重度COPD仍有可能检查结果正常[14]，因此肺功能一定程度上无法区分COPD的严重程度，进而对病情的准确评估存在一定缺陷，从而影响临床医师对疾病诊断及治疗方法的精准选择。随着影像设备、扫描技术（如低剂量扫描）及人工智能的迅速发展，研究表明[15]高分辨CT扫描结合人工智能通过对肺气肿指数、肺气肿分布区域、小气道病变等进行分析，一定程度上可以辅助临床医师对COPD严重程度的诊断。同时最新指南中提出部分低收入地区肺功能检查并非为COPD诊断的常规检查，进一步强调了CT检查的重要性。
研究发现[11，16]基于体素CT定量指标对COPD的诊断及预后具有一定的临床价值，COPD的气流受限主要与肺气肿分布区域及严重程度、小气道病变有关。本研究发现基于体素双气相配准CT定量指标fSAD%，Emph%在轻度COPD组和重度COPD组之间存在显著差异，重度COPD组高于轻度COPD组。同时随着COPD严重程度增加，肺功能指标FVC、FEV1%pred和FEV1/FVC%均下降，提示fSAD%，Emph%可以反映COPD患者的肺损伤程度，即肺气肿和小气道病变区域越广，肺功能受损越严重。研究指出[17]小气道病变、肺气肿区域及分布范围与COPD严重程度及肺功能指标相关，与此研究结果相似。
此外，作者发现重度COPD组吸烟指数高于轻度COPD组，与之对应重度COPD组fSAD%和Emph%高于轻度COPD组，表明吸烟程度加重肺损伤程度，这可能与吸入烟尘微颗粒物和气体增加了肺的负担有关，前文提及的2021年最新指南根据最近5年发表文章证实吸烟对COPD存在极大的影响。部分学者指出[18−20] 肺气肿与吸烟关系密切，同时有COPD家族史的发病率增加3倍左右，基因机制可能miR155 HG可以通过调节miR128 5 p/BRD4轴,加重烟雾相关COPD中人肺微血管内皮细胞的凋亡和炎症，从而促进、加重COPD的病情。
通过Logistic回归分析发现，吸烟指数及CT定量指标是重度COPD的独立风险因素。本研究将分析所得四项危险因素进行整合，建议预测重度COPD的列线图模型，经校准曲线验证，模型校准曲线与标准曲线接近，提示模型的校准能力好，反映模型预测重度COPD风险与实际风险一致程度高，绘制的列线表中C-index为0.786（95%CI：
0.7040.867 ），灵敏度0.673，特异度0.788，AUC为0.786，当C-index越接近1表示区分度越好，即此模型具有较好的准确性和区分度，表明该列线图预测COPD严重程度的效能较好，为临床判断不同COPD分级的风险度提供客观依据。本研究表明，基于体素的CT定量指标可以作为评估疾病严重程度与长期随访的有效工具，再一定程度上可以定量监测疾病的发生发展。有研究[21]通过对慢阻肺合并肺结核患者的危险因素建立列线图模型得出此模型具有较好的预测效能，在一定程度上可预测慢阻肺合并肺结核患者。最近关于冠状病毒病 (COVID-19) 的文献研究[22]通过建立预测模型并构建了一个列线图来预测COVID-19 患者的住院生存率发现该模型具有良好的性能，可在临床上用于COVID-19 的管理。因此从不同角度验证了本文结论的可靠性。
本研究存在一定的局限性：（1）样本量小，该预测模型还需要经多中心，更大样本量的研究进一步验证。（2）预测变量局限，还需进一步纳入更为全面的实验室指标及CT定量指标。例如，有研究表明[23]气道壁的厚度与肺功能的分级有明显相关性，即在不同分级中气道壁厚度逐渐增加。未来也需要以此为中心进行深入研究。
综上所述，基于CT体素的定量指标可预测COPD严重程度，通过CT定量指标和吸烟指数建立预测COPD严重程度的列线图模型具有良好的诊断效能。
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图 2 基于深度学习的CT重建算法分类
Figure 2. Classification of Deep Learning-based CT Reconstruction Algorithms
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图 3 基于深度学习的LDCT图像后处理算法分类
Figure 3. Classification of deep learning-based LDCT image post-processing algorithms
1 典型的CT成像算法在现有数据集上性能比较
方法 主要特点 优点 缺点 传统 CT 重建算法 FBP 解析类且最基础的重建算法 成像速度快，鲁棒性好 对稀疏角度 CT 重建质量不佳 TV-POCS 迭代重建类算法，使用了 TV 正则化项进行约束 研究对象为原始数据，不容易丢失信息，降噪效果优于 FBP 算法 降噪结果容易产生块状伪影，且部分重要的细微结构被平滑 传统 CT 后处理算法 BM3D 基于块匹配的后处理 细节保留能力优于 TV- POCS 降噪结果出现了模糊与失真 K-SVD 基于字典学习的后处理 算法运算时间较长，降噪结果中仍然存在部分伪影 基于深度学习的 CT 重建算法 iCT-Net 用 CNN 学习 FBP：投影数据扩展；滤波；反投影；求和 对稀疏角度、短扫描内部扫描 CT 重建效果良好 并未解决锥束 CT 重建问题，网络参数较多 LEARN 迭代展开类算法，对“fields of experts”进行展开，并用 CNN 进行学习 重建结果保留了更多的边缘与细节，比传统迭代算法更高效 鲁棒性较差，对正则化函数具有一定的限制 DRONE 双域残差优化网络 重建精度高 需要更多数据集进行训练 基于深度学习的 CT 后处理算法 RED-CNN CNN 网络，包括 5 层编码与 5 层解码，其中初始输入、第 2、4 层编码端特征通过残差连接并入相应解码端 降噪效果优于传统后处理算法，降噪结果中伪影残留量较少 降噪结果容易产生图像过平滑现象，丢失了一些细微信息，如血管等 WGAN-VGG GAN 网络，G：8 层 conv，D：6 层 conv，2 层 FC，损失函数：WGAN+VGG 训练稳定性较好，能够有效缓解图像过平滑问题 在抑制伪影的过程中易破坏图像原有结构，引入新的噪声 SACNN CNN 网络，同时采用自注意力与自编码模块 在伪影抑制与结构保留方面实现了较好的平衡 降噪结果中仍然存在部分噪声，细微结构产生了失真 
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表 1 典型的CT成像算法在现有数据集上性能比较
Table 1 Performance comparison of typical CT imaging algorithms on existing data sets
方法 主要特点 优点 缺点 传统 CT 重建算法 FBP 解析类且最基础的重建算法 成像速度快，鲁棒性好 对稀疏角度 CT 重建质量不佳 TV-POCS 迭代重建类算法，使用了 TV 正则化项进行约束 研究对象为原始数据，不容易丢失信息，降噪效果优于 FBP 算法 降噪结果容易产生块状伪影，且部分重要的细微结构被平滑 传统 CT 后处理算法 BM3D 基于块匹配的后处理 细节保留能力优于 TV- POCS 降噪结果出现了模糊与失真 K-SVD 基于字典学习的后处理 算法运算时间较长，降噪结果中仍然存在部分伪影 基于深度学习的 CT 重建算法 iCT-Net 用 CNN 学习 FBP：投影数据扩展；滤波；反投影；求和 对稀疏角度、短扫描内部扫描 CT 重建效果良好 并未解决锥束 CT 重建问题，网络参数较多 LEARN 迭代展开类算法，对“fields of experts”进行展开，并用 CNN 进行学习 重建结果保留了更多的边缘与细节，比传统迭代算法更高效 鲁棒性较差，对正则化函数具有一定的限制 DRONE 双域残差优化网络 重建精度高 需要更多数据集进行训练 基于深度学习的 CT 后处理算法 RED-CNN CNN 网络，包括 5 层编码与 5 层解码，其中初始输入、第 2、4 层编码端特征通过残差连接并入相应解码端 降噪效果优于传统后处理算法，降噪结果中伪影残留量较少 降噪结果容易产生图像过平滑现象，丢失了一些细微信息，如血管等 WGAN-VGG GAN 网络，G：8 层 conv，D：6 层 conv，2 层 FC，损失函数：WGAN+VGG 训练稳定性较好，能够有效缓解图像过平滑问题 在抑制伪影的过程中易破坏图像原有结构，引入新的噪声 SACNN CNN 网络，同时采用自注意力与自编码模块 在伪影抑制与结构保留方面实现了较好的平衡 降噪结果中仍然存在部分噪声，细微结构产生了失真
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