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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随着年龄的增长，腰椎退行性变及椎间盘病变日趋增多，CT检查能及时发现诊断腰椎病变并能随访治疗效果，但CT检查辐射问题一直为人们所关注，随着患者受辐射剂量的增加，癌症的发生概率会增大，腰椎CT扫描范围包括性腺，而人体性腺对辐射最敏感，所以开展低剂量腰椎CT检查非常必要。
以往研究均是通过降低管电压或者降低管电流来降低辐射剂量，因腰椎体层较厚，降低管电压或管电流会导致图像噪声增加。本文为解决腰椎CT高辐射剂量及图像噪声偏高的问题，采用最新的能谱纯化技术结合高级模拟迭代重建（ADMIRE）技术，探讨如何更好的优化腰椎CT检查的图像质量和降低辐射剂量。
1. 材料与方法
1.1 一般资料
选取2021年8月至2022年5月因腰痛来我院行腰椎CT检查的患者，在检查前计算患者的体质量指数（bodymassindex，BMI），BMI=体重（kg）/身高（m）2。纳入年龄在25～65岁，BMI在18.5～25 kg/m2的患者，排除有腰椎手术史和腰椎畸形及有椎体金属植入物的患者，共收集88例。对照组（A组）、试验组（B组）每组44例。
A组与B组平均年龄分别为（45.9±12.1）岁和（47.2±13.8）岁。两组间年龄差异无统计学意义，A组与B组平均BMI分别为（20.1±2.89）kg/m２和（21.40±3.50）kg/m２。
1.2 扫描方法
采用德国SOMATOM Force第3代双源CT，扫描范围从胸12椎体至骶1椎体。扫描参数：对照组（A组）管电压120 kV，参考管电流350 mAs；试验组（B组）管电压Sn 150 kV，参考管电流350 mAs，其他扫描参数均一致。
重建采用高级模拟迭代重建算法（ADMIRE），重建等级3级，重建薄层图像，层厚1 mm，层间距0.60 mm，软组织窗采用软组织算法，卷积核Br40，骨窗采用骨算法，卷积核Br64，重建图像窗宽，窗位分别为350 HU和50 HU（软组织窗）、2500 HU和800 HU（骨窗）。所有图像重建完成后自动发至西门子Syngovia VB20A后处理工作站。
1.3 图像质量评价
1.3.1 客观评价
由1名主管技师从工作站中取L3椎体正中层面，在软组织窗上测量腰大肌与竖脊肌的CT值和噪声，腰大肌的噪声为SD1，竖脊肌的噪声为SD2，噪声值用对应所测的标准差表示，并计算信噪比（SNR）：
$$ {\rm{SNR}}=腰大肌\;{\rm{CT}}\;值/{\rm{SD}}1。$$ (1) 1.3.2 主观评价
由3名副主任及以上诊断医师双盲法进行评分。评价L3/4层面椎间盘、椎间孔、黄韧带、硬膜囊及小关节图像质量。评价标准[1]：2分（软组织结构清晰，其边缘清楚，无伪影，且诊断明确）；1分（软组织结构清晰，边缘欠清，有轻度伪影，但尚可诊断）；0分（软组织结构不清，边缘模糊，伪影较重，不能进行诊断）。
1.4 辐射剂量
统计设备记录的容积CT剂量指数（CT dose index volumes，CTDIvol）及剂量长度乘积（dose length product，DLP），并计算有效辐射剂量（effective dose，ED）[2]，计算公式：
$$ {\rm{ED}}={\rm{DLP}}\times k（k=0.011\;{\rm{mSv}}\cdot{\rm{mGy}}\cdot{\rm{cm}}）。$$ (2) 1.5 统计学分析
1.5.1 客观评价和辐射剂量统计分析
采用SPSS 26.0软件对数据进行统计学分析。连续性数据非正态分布数据两组间比较采用Mann-Whitney U检验，用中位数及四分位数（M(Q25,Q75)）表示。双侧检验，以P<0.05为差异有统计学意义。
1.5.2 主观评价
采用组内相关系数（intraclass correlation coefficient，ICC）对3位诊断医师的评分结果一致性进行分析。ICC介于0和1之间，ICC大于0.75表示一致性较好。
2. 结果
2.1 客观评价结果
两组图像腰大肌的CT值、竖脊肌的CT值和噪声（SD2）、SNR均存在统计学差异，而腰大肌的噪声（SD1）不具有统计学差异（表1）；图1为120 kV轴位上噪声和CT值测量及矢状位重组图，图2为Sn 150 kV下的轴位上噪声和CT值测量测量及矢状位重组图。
表 1 A组和B组图像质量客观评价表Table 1. Objective evaluation of image quality in groups A and B项目 组别 统计检验 A组 B组 Z P 腰大肌/HU 53.00（48.70～56.00） 47.90（43.70～51.00） 2.741 0.016 SD1 5.73（4.83～6.83） 5.09（4.69～5.24） 1.904 0.057 竖脊肌/HU 52.00（46.2～55.00） 43.50（38.20～51） 3.511 <0.001 SD2 5.41（5.27～5.98） 4.56（3.62～5.63） 3.964 <0.001 SNR 9.12（7.88～10.51） 9.86（7.95～10.02） -0.693 0.488 ![]()
图 1 管电压120 kV下CT值和噪声测量及矢状位重组图（重组层厚1 mm、间隔0.6 mm）Figure 1. CT value, noise measurement, and sagittal position recombination at 120 kV tube voltage (recombination layer thickness 1 mm, interval 0.6 mm)![]()
图 2 管电压Sn 150 kV下CT值和噪声测量及矢状位重组图（重组层厚1 mm、间隔0.6 mm）Figure 2. CT value, noise measurement, and sagittal position recombination at tube voltage Sn 150 kV (recombination layer thickness 1 mm, interval 0.6 mm)2.2 主观评价
3位医师对椎间盘、椎间孔、黄韧带、硬膜囊及小关节及整体图像质量评价均无统计学差异（表2），说明两组图像质量医师主观评价无差异，且均能符合医师诊断要求。
表 2 3位诊断医师的主观评分统计分析表Table 2. Statistical analysis of the subjective scores from the three doctors interpreting the computed tomography images指标 组别 P A组 B组 椎间盘 2.00±0.00 2.00±0.00 >0.999 椎间孔 1.98±0.15 1.98±0.15 0.156 黄韧带 1.95±0.21 2.00±0.00 0.562 硬膜囊 1.98±0.15 1.95±0.21 >0.999 小关节图像 2.00±0.00 2.00±0.00 0.320 整体图像质量 2.00±0.00 2.00±0.00 >0.999 2.3 辐射剂量
两组辐射剂量DLP、ED有统计学差异，两组辐射剂量差异明显，B组DLP值比A组降低了32.27％，B组ED值比A组降低了30.31％（表3）。
表 3 A组和B组辐射剂量统计表Table 3. Radiation dose in groups A and B项目 组别 统计检验 A组 B组 Z P mAs 333.00（300.00～362.00） 237.50（222.00～261.00） 7.885 <0.001 CTDIvol 14.75（13.65～16.00） 6.57（5.20～7.23） 8.015 <0.001 DLP 413.60（351.00～425.50） 280.13（230.89～327.20） 6.946 <0.001 ED 4.55（3.86～4.68） 3.08（2.54～3.60） 6.946 <0.001 3. 讨论
腰椎因体层相对较厚，需要高管电压来增加X线的穿透力，高管电流来降低图像的噪声，造成腰椎CT辐射剂量往往较高，以往研究都是通过降低管电流来降低辐射剂量。随着设备和技术的进步，众多新的降低辐射剂量的技术出现，如：低管电压[3-4]、自动管电流[5-6]、高级迭代重建算法[7]、能谱纯化[8]等，这些技术为我们开展低剂量CT提供了条件。
本研究B组管电压是用能谱纯化Sn 150 kV，而A组管电压是用120 kV，统计结果显示B组的辐射剂量低于A组30.31％。因为A组120 kV的X线球管是用铜和铝滤过，Sn 150 kV的X线球管是用能谱纯化技术的锡滤过，锡的原子序数比铜和铝高，锡滤过板能过滤掉X线球管的低能级射线，提高射线能量，而对人体产生辐射的主要是低能级软射线，低能级软射线以光电效应为主，大部分被人体吸收产生辐射。能谱纯化技术只保留了对人体成像有用的高能级射线，高能级射线会穿过人体相对辐射较少，所以B组辐射剂量低于A组，多学者也证实了这一说法[9-13]。
客观评价中A组肌肉的噪声要高于B组，腰大肌的噪声两组之间无统计学差异，而竖脊肌的噪声两组之间有统计学差异，此结果说明射线能量和图像噪声成正相关，也证实了Sn 150 kV的穿透力较120 kV的好。因竖脊肌处于腰大肌的下层，射线先穿过腰大肌再到竖脊肌，射线能量会因组织的阻挡发生衰减，A组射线的能量到达竖脊肌时比B组衰减更多，因衰减后的能量差异造成了噪声值的差异，故造成了两组不同肌肉之间统计学结果的差异。
沈梓璇等[14]论述了120 kVp管电压所获得的腰椎图像质量评分以及信噪比皆较高，但辐射剂量也较大的观点。本文为了解决这一问题，首次采用Sn 150 kV用于腰椎CT检查，主观评价结果显示，3位观察者的ICC为0.829，表示为两组图像主观评价一致性较好，说明两组图像质量均满足诊断要求，主客观评价结果均证实了Sn 150 kV用于腰椎CT检查是可行的。王帅等[15]也证实Sn 150 kV能用于全腹部CT检查，且辐射剂量较低，与本文研究结果一致。
高级模拟迭代重建，是将原始图像中的原始数据噪声投射到图像中，得到的图像是多次迭代重建后的组合，再将原始数据进行准确的图像校正，对原始数据域进行去噪及去除伪影，最后进行图像域的校正，反复迭代来降低噪声，图像空间分辨率不受影响。客观评价表中A组和B组图像的噪声均值都处于10以下，证实了高级模拟迭代重建的降噪能力。顾海峰等[16]和Schlunk等[17]也证明了迭代重建能降低噪声保证图像质量满足诊断需求。
综上所述，采用能谱纯化Sn 150 kV结合ADMIRE，不但能有效减低辐射剂量，还可保证优质的图像质量，值得在成人腰椎CT中推广使用。
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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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