Zhang Hua, Chen Kai, Xu Xiaotong, You Tao, Sun Wenzheng, Dang Jun
School of Biomedical Engineering, Southern Medical University, Guang Zhou, Guangdong, China.
Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guang Zhou, Guangdong, China.
Front Oncol. 2024 Aug 5;14:1390398. doi: 10.3389/fonc.2024.1390398. eCollection 2024.
To enhance the accuracy of real-time four-dimensional cone beam CT (4D-CBCT) imaging by incorporating spatiotemporal correlation from the sequential projection image into the single projection-based 4D-CBCT estimation process.
We first derived 4D deformation vector fields (DVFs) from patient 4D-CT. Principal component analysis (PCA) was then employed to extract distinctive feature labels for each DVF, focusing on the first three PCA coefficients. To simulate a wide range of respiratory motion, we expanded the motion amplitude and used random sampling to generate approximately 900 sets of PCA labels. These labels were used to produce 900 simulated 4D-DVFs, which in turn deformed the 0% phase 4D-CT to obtain 900 CBCT volumes with continuous motion amplitudes. Following this, the forward projection was performed at one angle to get all of the digital reconstructed radiographs (DRRs). These DRRs and the PCA labels were used as the training data set. To capture the spatiotemporal correlation in the projections, we propose to use the convolutional LSTM (ConvLSTM) network for PCA coefficient estimation. For network testing, when several online CBCT projections (with different motion amplitudes that cover the full respiration range) are acquired and sent into the network, the corresponding 4D-PCA coefficients will be obtained and finally lead to a full online 4D-CBCT prediction. A phantom experiment is first performed with the XCAT phantom; then, a pilot clinical evaluation is further conducted.
Results on the XCAT phantom and the patient data show that the proposed approach outperformed other networks in terms of visual inspection and quantitative metrics. For the XCAT phantom experiment, ConvLSTM achieves the highest quantification accuracy with MAPE(Mean Absolute Percentage Error), PSNR (Peak Signal-to-Noise Ratio), and RMSE(Root Mean Squared Error) of 0.0459, 64.6742, and 0.0011, respectively. For the patient pilot clinical experiment, ConvLSTM also achieves the best quantification accuracy with that of 0.0934, 63.7294, and 0.0019, respectively. The quantification evaluation labels that we used are 1) the Mean Absolute Error (MAE), 2) the Normalized Cross Correlation (NCC), 3)the Structural Similarity Index Measurement(SSIM), 4)the Peak Signal-to-Noise Ratio (PSNR), 5)the Root Mean Squared Error(RMSE), and 6) the Absolute Percentage Error (MAPE).
The spatiotemporal correlation-based respiration motion modeling supplied a potential solution for accurate real-time 4D-CBCT reconstruction.
通过将序列投影图像中的时空相关性纳入基于单投影的4D-CBCT估计过程，提高实时四维锥束CT（4D-CBCT）成像的准确性。
我们首先从患者的4D-CT中导出4D变形矢量场（DVF）。然后采用主成分分析（PCA）为每个DVF提取独特的特征标签，重点关注前三个PCA系数。为了模拟广泛的呼吸运动，我们扩大了运动幅度，并使用随机采样生成大约900组PCA标签。这些标签用于生成900个模拟的4D-DVF，进而使0%相位的4D-CT变形，以获得900个具有连续运动幅度的CBCT体积。随后，在一个角度进行前向投影以获取所有数字重建射线照片（DRR）。这些DRR和PCA标签用作训练数据集。为了捕捉投影中的时空相关性，我们建议使用卷积长短期记忆（ConvLSTM）网络进行PCA系数估计。在网络测试中，当获取并将几个在线CBCT投影（具有覆盖整个呼吸范围的不同运动幅度）发送到网络中时，将获得相应的4D-PCA系数，最终实现完整的在线4D-CBCT预测。首先使用XCAT体模进行体模实验；然后，进一步进行初步临床评估。
XCAT体模和患者数据的结果表明，所提出的方法在视觉检查和定量指标方面优于其他网络。对于XCAT体模实验，ConvLSTM在平均绝对百分比误差（MAPE）、峰值信噪比（PSNR）和均方根误差（RMSE）方面分别达到0.0459、64.6742和0.0011，实现了最高的量化精度。对于患者初步临床实验，ConvLSTM在平均绝对误差（MAE）、归一化互相关（NCC）、结构相似性指数测量（SSIM）、峰值信噪比（PSNR）、均方根误差（RMSE）和绝对百分比误差（MAPE）方面也分别达到0.0934、63.7294和0.0019，实现了最佳的量化精度。我们使用的量化评估指标包括：1）平均绝对误差（MAE），2）归一化互相关（NCC），3）结构相似性指数测量（SSIM），4）峰值信噪比（PSNR），5）均方根误差（RMSE），6）绝对百分比误差（MAPE）。
基于时空相关性的呼吸运动建模为准确的实时4D-CBCT重建提供了一种潜在的解决方案。
