CT肺动脉造影中的不确定性量化
Uncertainty Quantification in CT pulmonary angiography
AI总结：
针对CT肺栓塞检测中伪影与真实病灶难以区分的问题，提出可扩展假设检验与贝叶斯框架，以量化候选PE结构的不确定性，并验证其在高噪声、少数据条件下的能力。
摘要：
胸部计算机断层扫描（CT）成像被广泛用于肺栓塞（PE）的检测和监测。然而，由于采集或图像重建过程中涉及的处理环节，CT图像可能包含伪影。放射科医生通常必须区分此类伪影与真正的PE。我们的主要贡献是一种面向CT的可扩展假设检验方法，以实现对可能PE的不确定性量化。具体而言，我们引入了一个贝叶斯框架，用于量化可被识别为PE的观测致密结构的不确定性。我们评估了该方法在高噪声环境和数据不足情况下的运行能力。
英文摘要：
Computed tomography (CT) imaging of the thorax is widely used for the detection and monitoring of pulmonary embolism (PE). However, CT images can contain artifacts due to the acquisition or the processes involved in image reconstruction. Radiologists often have to distinguish between such artifacts and actual PEs. Our main contribution comes in the form of a scalable hypothesis testing method for CT, to enable quantifying uncertainty of possible PEs. In particular, we introduce a Bayesian Framework to quantify the uncertainty of an observed compact structure that can be identified as a PE. We assess the ability of the method to operate under high noise environments and with insufficient data.
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