Task-Based Optimization of CT Trajectories Using a Learned Defect Visibility Metric

Task-Based Optimization of CT Trajectories Using a Learned Defect Visibility Metric

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Optimizing computed tomography (CT) trajectories is critical in scenarios where traditional scan paths are impractical or infeasible.In such cases, alternative trajectory designs are required to ensure accurate reconstructions, especially for capturing critical details such as defects or regions of interest.Standard methods often fail to achieve high Tuy completeness, a key metric for reliable imaging, and struggle to prioritize information retention My Mom Dad Is Well Trained Funny Cartoon Dog Gift For Dog Lover Personalized Shirt in specific areas.Trajectory optimization is therefore essential for applications focused on defect detection or targeted imaging of regions of interest.

In this study, we propose a novel approach that uses defect detection probability as the primary metric for optimizing CT trajectories.Our methodology begins by embedding artificial defects in CAD volumes to simulate defect scenarios, which are then used to train and test a ResNet-18 model.The model predicts the likelihood of defect presence in each projection, and these predictions guide a greedy optimization process that incorporates Tuy completeness to ensure sufficient information for accurate here reconstruction.The resulting CT trajectory achieves high Tuy completeness while prioritizing areas of interest marked by artificial defects.

This approach reduces scan time and cost while improving reconstruction efficiency compared to random projection selection.Our results demonstrate that this method effectively optimizes CT trajectories by focusing on predefined regions of interest and provides a practical solution for efficient, cost-effective CT imaging in real-world applications.