PDF(Pubmed)
Abstract：
UNASSIGNED: X-ray scatter significantly affects the image quality of cone beam computed tomography (CBCT). Although convolutional neural networks (CNNs) have shown promise in correcting x-ray scatter, their effectiveness is hindered by two main challenges: the necessity for extensive datasets and the uncertainty regarding model generalizability. This study introduces a task-based paradigm to overcome these obstacles, enhancing the application of CNNs in scatter correction.
UNASSIGNED: Using a CNN with U-net architecture, the proposed methodology employs a two-stage training process for scatter correction in CBCT scans. Initially, the CNN is pre-trained on approximately 4000 image pairs from geometric phantom projections, then fine-tuned using transfer learning (TL) on 250 image pairs of anthropomorphic projections, enabling task-specific adaptations with minimal data. 2D scatter ratio (SR) maps from projection data were considered as CNN targets, and such maps were used to perform the scatter prediction. The fine-tuning process for specific imaging tasks, like head and neck imaging, involved simulating scans of an anthropomorphic phantom and pre-processing the data for CNN retraining.
UNASSIGNED: For the pre-training stage, it was observed that SR predictions were quite accurate (SSIM≥0.9). The accuracy of SR predictions was further improved after TL, with a relatively short retraining time (≈70 times faster than pre-training) and using considerably fewer samples compared to the pre-training dataset (≈12 times smaller).
UNASSIGNED: A fast and low-cost methodology to generate task-specific CNN for scatter correction in CBCT was developed. CNN models trained with the proposed methodology were successful to correct x-ray scatter in anthropomorphic structures, unknown to the network, for simulated data.
UNASSIGNED: Using a CNN with U-net architecture, the proposed methodology employs a two-stage training process for scatter correction in CBCT scans. Initially, the CNN is pre-trained on approximately 4000 image pairs from geometric phantom projections, then fine-tuned using transfer learning (TL) on 250 image pairs of anthropomorphic projections, enabling task-specific adaptations with minimal data. 2D scatter ratio (SR) maps from projection data were considered as CNN targets, and such maps were used to perform the scatter prediction. The fine-tuning process for specific imaging tasks, like head and neck imaging, involved simulating scans of an anthropomorphic phantom and pre-processing the data for CNN retraining.
UNASSIGNED: For the pre-training stage, it was observed that SR predictions were quite accurate (SSIM≥0.9). The accuracy of SR predictions was further improved after TL, with a relatively short retraining time (≈70 times faster than pre-training) and using considerably fewer samples compared to the pre-training dataset (≈12 times smaller).
UNASSIGNED: A fast and low-cost methodology to generate task-specific CNN for scatter correction in CBCT was developed. CNN models trained with the proposed methodology were successful to correct x-ray scatter in anthropomorphic structures, unknown to the network, for simulated data.
摘要：
■X射线散射显着影响锥形束计算机断层扫描（CBCT）的图像质量。尽管卷积神经网络（CNN）在校正X射线散射方面表现出了希望，它们的有效性受到两个主要挑战的阻碍:大量数据集的必要性和模型泛化的不确定性。本研究引入了一种基于任务的范式来克服这些障碍，增强CNN在散射校正中的应用。
■使用带有U网架构的CNN，所提出的方法采用两阶段训练过程在CBCT扫描中进行散射校正。最初，CNN通过几何幻影投影对大约4000个图像对进行预训练，然后使用迁移学习（TL）对250对拟人化投影进行微调，以最少的数据实现特定任务的适应。来自投影数据的2D散射比(SR)图被认为是CNN目标，这样的地图被用来进行散射预测。特定成像任务的微调过程，比如头颈部成像，涉及模拟拟人化体模的扫描并预处理数据以进行CNN再训练。
■对于预训练阶段，观察到SR预测相当准确(SSIM≥0.9).在TL，SR预测的准确性进一步提高。与训练前的数据集相比，再训练时间相对较短（比预训练快约70倍），并且使用的样本要少得多（约小12倍）。
■开发了一种快速且低成本的方法，用于生成针对CBCT中散射校正的特定任务CNN。用所提出的方法训练的CNN模型成功地校正了拟人结构中的X射线散射，网络未知，用于模拟数据。
■使用带有U网架构的CNN，所提出的方法采用两阶段训练过程在CBCT扫描中进行散射校正。最初，CNN通过几何幻影投影对大约4000个图像对进行预训练，然后使用迁移学习（TL）对250对拟人化投影进行微调，以最少的数据实现特定任务的适应。来自投影数据的2D散射比(SR)图被认为是CNN目标，这样的地图被用来进行散射预测。特定成像任务的微调过程，比如头颈部成像，涉及模拟拟人化体模的扫描并预处理数据以进行CNN再训练。
■对于预训练阶段，观察到SR预测相当准确(SSIM≥0.9).在TL，SR预测的准确性进一步提高。与训练前的数据集相比，再训练时间相对较短（比预训练快约70倍），并且使用的样本要少得多（约小12倍）。
■开发了一种快速且低成本的方法，用于生成针对CBCT中散射校正的特定任务CNN。用所提出的方法训练的CNN模型成功地校正了拟人结构中的X射线散射，网络未知，用于模拟数据。
