论文标题
低剂量计算机断层扫描图像重建的条件标准化流量
Conditional Normalizing Flows for Low-Dose Computed Tomography Image Reconstruction
论文作者
论文摘要
来自计算机断层扫描（CT）测量的图像重建是一个具有挑战性的统计反问题，因为需要估计高维条件分布。基于从高质量重建获得的训练数据，我们旨在从嘈杂的低剂量CT测量值中学习有条件的图像密度。为了解决此问题，我们提出了一个混合条件归一化流，该流程通过使用过滤后的预测作为调节剂来整合物理模型。我们在低剂量CT基准上评估了我们的方法，与其他基于深度学习的方法相比，基于流的方法的结构相似性表现出了卓越的性能。
Image reconstruction from computed tomography (CT) measurement is a challenging statistical inverse problem since a high-dimensional conditional distribution needs to be estimated. Based on training data obtained from high-quality reconstructions, we aim to learn a conditional density of images from noisy low-dose CT measurements. To tackle this problem, we propose a hybrid conditional normalizing flow, which integrates the physical model by using the filtered back-projection as conditioner. We evaluate our approach on a low-dose CT benchmark and demonstrate superior performance in terms of structural similarity of our flow-based method compared to other deep learning based approaches.
