We present a method for selecting valuable projections in computed tomography (CT) scans to enhance image reconstruction and diagnosis. The approach integrates two important factors, projection-based detectability and data completeness, into a single feed-forward neural network. The network evaluates the value of projections, processes them through a differentiable ranking function and makes the final selection using a straight-through estimator. Data completeness is ensured through the label provided during training. The approach eliminates the need for heuristically enforcing data completeness, which may exclude valuable projections. The method is evaluated on simulated data in a non-destructive testing scenario, where the aim is to maximize the reconstruction quality within a specified region of interest. We achieve comparable results to previous methods, laying the foundation for using reconstruction-based loss functions to learn the selection of projections.

翻译：我们提出一种用于计算机断层扫描（CT）中选取高价值投影的方法，以提升图像重建与诊断效能。该方法将基于投影的检测能力与数据完整性两个关键因素整合至单一前馈神经网络中。该网络评估投影的价值，通过可微排名函数处理这些投影，并利用直通估计器完成最终选择。训练期间提供的标注确保数据完整性。该方法无需通过启发式规则强制维持数据完整性，从而避免排除有价值的投影。在无损检测场景的模拟数据评估中，本研究旨在最大化指定感兴趣区域内的重建质量。实验结果达到与现有方法相当的水平，为基于重建损失函数学习投影选择奠定了基础。