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：我们提出了一种选择计算机断层扫描（CT扫描）中有价值的投影来增强图像重建和诊断的方法。该方法将基于投影的可检测性和数据完整性这两个重要因素集成到一个前馈神经网络中。该网络评估投影的价值，通过可微分排序功能对其进行处理，并使用直通估计器进行最终选择。通过训练期间提供的标签来确保数据完整性。该方法消除了启发式强制数据完整性的需要，从而可能排除有价值的投影。该方法在一个非破坏性测试场景中对模拟数据进行了评估，其目的是在指定的感兴趣区域内最大化重建质量。我们实现了与之前方法相当的结果，为使用基于重建的损失函数来学习投影选择奠定了基础。