低剂量计算机断层扫描的物理/模型和数据驱动方法:一项调查
Wenjun Xia1, Hongming Shan2, Ge Wang3
1School of Cyber Science and Engineering, Sichuan University, Chengdu 610065, China.
概括
深度学习 (DL) 在低剂量计算机断层扫描 (LDCT) 中表现有希望,但面临挑战. 基于混合物理和数据驱动的方法为更稳定和可靠的LDCT成像提供了解决方案.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算科学 计算科学
背景情况:
- 自2016年以来,深度学习 (DL) 已经显著提升了断层成像,特别是低剂量计算机断层成像 (LDCT).
- 然而,纯粹的DL方法用于LDCT的消毒和重建面临诸如"黑子"性质和不稳定性等挑战,阻碍了临床应用.
研究的目的:
- 系统地审查LDCT的物理/基于模型的数据驱动方法.
- 总结这些混合方法的损失函数和培训策略.
- 评估性能,并讨论混合LDCT方法的未来方向.
主要方法:
- 综合成像物理和LDCT模型的混合深度学习模型的审查.
- 分析这些混合网络中使用的各种损失函数和培训策略.
- 对LDCT的不同基于物理的DL方法的性能评估.
主要成果:
- 结合物理/基于模型和数据的元素的混合方法显示出克服LDCT中纯DL的局限性的潜力.
- 整合物理原理提高了DL模型对LDCT的稳定性和可解释性.
- 系统审查提供了当前趋势和挑战的全面概述.
结论:
- 混合物理/基于模型的数据驱动方法代表了推进低剂量CT成像的有希望的方向.
- 解决不稳定性和可解释性问题对于DL在LDCT的临床翻译至关重要.
- 需要进一步的研究来优化这些混合方法,并探索未来的方向.
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