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.
■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。