论文标题
针对各向异性X射线暗场层摄影的特定任务性能预测和采集优化
Task-specific Performance Prediction and Acquisition Optimization for Anisotropic X-ray Dark-field Tomography
论文作者
论文摘要
各向异性X射线深色场断层扫描（AXDT）是一种最近开发的成像模式，可以使用基于实验室的X射线光栅干涉仪设置来可视化定向微观结构。尽管存在非常有希望的应用方案，例如，在纤维复合材料的材料测试或脑细胞连接性的医学诊断中，AXDT由于需要对各向异性X射线散射功能进行全面采样所需的复杂且耗时的收购，因此面临实际适用性的挑战。但是，根据手头的特定成像任务，可能不需要完整的采样，从而减少了采集。在这项工作中，我们正在研究使用特定于任务可检测性指数的AXDT的性能预测方法。基于这种方法，我们提出了一种任务驱动的采集优化方法，该方法可以减少采集方案，同时保持特定于任务的图像质量较高。我们证明了该方法在模拟和实验数据的实验中的可行性和功效。
Anisotropic X-ray Dark-field Tomography (AXDT) is a recently developed imaging modality that enables the visualization of oriented microstructures using lab-based X-ray grating interferometer setups. While there are very promising application scenarios, for example in materials testing of fibrous composites or in medical diagnosis of brain cell connectivity, AXDT faces challenges in practical applicability due to the complex and time-intensive acquisitions required to fully sample the anisotropic X-ray scattering functions. However, depending on the specific imaging task at hand, a full sampling may not be required, allowing for reduced acquisitions. In this work we are investigating a performance prediction approach for AXDT using task-specific detectability indices. Based on this approach we present a task-driven acquisition optimization method that enables reduced acquisition schemes while keeping the task-specific image quality high. We demonstrate the feasibility and efficacy of the method in experiments with simulated and experimental data.
