论文标题
低剂量计算机断层扫描的物理/基于模型和数据驱动的方法：调查
Physics-/Model-Based and Data-Driven Methods for Low-Dose Computed Tomography: A survey
论文作者
论文摘要
自2016年以来，深度学习（DL）具有高级层析成像，尤其是在低剂量计算机断层扫描（LDCT）成像中。尽管受到大数据的驱动，但LDCT DeNoising和纯净的端到端重建网络通常会遭受黑匣子性质和主要问题，例如不稳定性，这是在低剂量CT应用中应用深度学习方法的主要障碍。新兴的趋势是将成像物理和模型整合到深网中，从而使基于物理/模型和数据驱动元素的杂交。 ％这种类型的混合方法已变得越来越有影响力。在本文中，我们系统地回顾了基于物理/模型的LDCT数据驱动方法，总结损失功能和培训策略，评估不同方法的性能，并讨论相关问题和未来的方向。
Since 2016, deep learning (DL) has advanced tomographic imaging with remarkable successes, especially in low-dose computed tomography (LDCT) imaging. Despite being driven by big data, the LDCT denoising and pure end-to-end reconstruction networks often suffer from the black box nature and major issues such as instabilities, which is a major barrier to apply deep learning methods in low-dose CT applications. An emerging trend is to integrate imaging physics and model into deep networks, enabling a hybridization of physics/model-based and data-driven elements. %This type of hybrid methods has become increasingly influential. In this paper, we systematically review the physics/model-based data-driven methods for LDCT, summarize the loss functions and training strategies, evaluate the performance of different methods, and discuss relevant issues and future directions.
