对CT工件减少的全面审查:从传统的减少技术到深度学习方法
Kai Chen1, Yinuo Wang2, Shuya Ji3
1School of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, China.
概括
本综述综合了计算机断层扫描 (CT) 的文物减少技术,对文物进行分类,并详细介绍了经典和深度学习方法. 它为理解和推进CT图像质量提供了一个框架,以实现可靠的诊断.
科学领域:
- 医疗成像医学成像
- 图像处理 图像处理
- 放射学 放射学是一门学科.
背景情况:
- 计算机断层扫描 (CT) 器件减少对于诊断可靠性至关重要.
- 现有的技术缺乏统一的,结构化的合成,特别是新兴的深度学习方法.
- 文物源于金属植入物,运动,探测器问题和散射,降低图像质量.
研究的目的:
- 提供CT人工物减少技术的结构化概述和分类.
- 审查基于传统和深度学习的文物减少策略.
- 确定CT人工物减少方面的挑战和未来研究方向.
主要方法:
- 将CT工件分为环,金属,运动和散射类型的分类.
- 通过CT成像链 (获取图像域) 审查传统的工件减少策略.
- 深度学习方法的分类和审查 (监督,半监督,无监督).
主要成果:
- 为经典的CT人工物减少技术建立了一个结构化的框架.
- 分析了深度学习方法的方法论贡献和局限性.
- 讨论临床应用和挑战,强调研究缺口.
结论:
- 介绍了CT人工物减少方法的全面综合.
- 该综述提供了一种结构化的方法来理解和开发新的技术.
- 未来的研究应该专注于解决改善临床实用性的关键挑战.
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