Deriving Pulmonary Ventilation Images From Clinical 4D-CBCT Using a Deep Learning-Based Model.
Deriving Pulmonary Ventilation Images From Clinical 4D-CBCT Using a Deep Learning-Based Model.
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使用基于深度学习的模型从临床 4D-CBCT 获取肺通气图像
DOI：
10.3389/fonc.2022.889266
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发表时间：
2022
影响因子：
4.7
通讯作者：
Dai, Jianrong
中科院分区：
文献类型：
--
作者：
Liu, Zhiqiang;Tian, Yuan;Miao, Junjie;Men, Kuo;Wang, Wenqing;Wang, Xin;Zhang, Tao;Bi, Nan;Dai, Jianrong
现有的4D锥形束计算机断层扫描（CBCT）通气图像测量算法受到变形图像配准（REDD）精度的影响。这项研究提出了一种新的深度学习（DL）方法，该方法不依赖于从4D-CBCT（CBCT-VI）中导出通气图像，该方法已通过金标准单光子发射计算机断层扫描通气图像（SPECT-VI）进行了验证。本研究包括28例食管癌或肺癌患者的4D-CBCT和99 mTc-Technegas SPECT/CT扫描。每个病人的扫描都是严格配准的。使用这些数据，CBCT-VI是使用基于深度学习的模型得出的。研究了两种类型的模型输入数据，即（a）4D-CBCT的10个相位和（B）4D-CBCT的峰值呼气和峰值吸气的两个相位。应用七重交叉验证来训练和评估模型。使用DIR依赖性方法（基于密度变化和基于雅可比的方法）测量CBCT-VI进行比较。使用逐体素斯皮尔曼相关性计算每个CBCT-VI和SPECT-VI之间的相关性。将通气图像分为高、中、低肺功能区。使用骰子相似系数（DSC）评价SPECT-VI和每个CBCT-VI之间不同功能肺区域的相似性。采用单因素方差分析模型对不同通气图像生成方法的平均DSC进行统计分析。密度变化、雅可比矩阵和深度学习方法的相关值分别为0.02 ± 0.10、0.02 ± 0.09和0.65 ± 0.13/0.65 ± 0.15，平均DSC值分别为0.34 ± 0.04、0.34 ± 0.03和0.59 ± 0.08/0.58 ± 0.09。与密度变化和雅可比方法相比，深度学习方法与SPECT-VI的相关性最强，相似性最高。结果表明，深度学习方法显著提高了相关性和相似性的准确性，衍生的CBCT-VI具有监测放射治疗过程中肺功能动态变化的潜力。
The current algorithms for measuring ventilation images from 4D cone-beam computed tomography (CBCT) are affected by the accuracy of deformable image registration (DIR). This study proposes a new deep learning (DL) method that does not rely on DIR to derive ventilation images from 4D-CBCT (CBCT-VI), which was validated with the gold-standard single-photon emission-computed tomography ventilation image (SPECT-VI). This study consists of 4D-CBCT and 99mTc-Technegas SPECT/CT scans of 28 esophagus or lung cancer patients. The scans were rigidly registered for each patient. Using these data, CBCT-VI was derived using a deep learning-based model. Two types of model input data are studied, namely, (a) 10 phases of 4D-CBCT and (b) two phases of peak-exhalation and peak-inhalation of 4D-CBCT. A sevenfold cross-validation was applied to train and evaluate the model. The DIR-dependent methods (density-change-based and Jacobian-based methods) were used to measure the CBCT-VIs for comparison. The correlation was calculated between each CBCT-VI and SPECT-VI using voxel-wise Spearman’s correlation. The ventilation images were divided into high, medium, and low functional lung regions. The similarity of different functional lung regions between SPECT-VI and each CBCT-VI was evaluated using the dice similarity coefficient (DSC). One-factor ANONA model was used for statistical analysis of the averaged DSC for the different methods of generating ventilation images. The correlation values were 0.02 ± 0.10, 0.02 ± 0.09, and 0.65 ± 0.13/0.65 ± 0.15, and the averaged DSC values were 0.34 ± 0.04, 0.34 ± 0.03, and 0.59 ± 0.08/0.58 ± 0.09 for the density change, Jacobian, and deep learning methods, respectively. The strongest correlation and the highest similarity with SPECT-VI were observed for the deep learning method compared to the density change and Jacobian methods. The results showed that the deep learning method improved the accuracy of correlation and similarity significantly, and the derived CBCT-VIs have the potential to monitor the lung function dynamic changes during radiotherapy.
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DOI：
10.1016/j.ijrobp.2016.02.058
发表时间：
2016-07-15
期刊：
International journal of radiation oncology, biology, physics
影响因子：
--
作者：
Vinogradskiy Y;Schubert L;Diot Q;Waxweiller T;Koo P;Castillo R;Castillo E;Guerrero T;Rusthoven C;Gaspar L;Kavanagh B;Miften M
通讯作者：
Miften M
影响因子：
3.8
作者：
Vinogradskiy, Yevgeniy Y.;Castillo, Richard;Guerrero, Thomas
通讯作者：
Guerrero, Thomas
DOI：
10.1016/j.ijrobp.2018.07.186
发表时间：
2018-11-15
期刊：
International journal of radiation oncology, biology, physics
影响因子：
--
作者：
Vinogradskiy Y;Rusthoven CG;Schubert L;Jones B;Faught A;Castillo R;Castillo E;Gaspar LE;Kwak J;Waxweiler T;Dougherty M;Gao D;Stevens C;Miften M;Kavanagh B;Guerrero T;Grills I
通讯作者：
Grills I
影响因子：
3.5
作者：
Guerrero, T;Sanders, K;Komaki, R
通讯作者：
Komaki, R
影响因子：
10.9
作者：
Reinhardt, Joseph M.;Ding, Kai;Bodas, Shalmali V.
通讯作者：
Bodas, Shalmali V.