图像质量改善和辐射剂量降低的CT图像无色化方法
Rabeya Tus Sadia1, Jin Chen2, Jie Zhang3
1Department of Computer Science, University of Kentucky, Lexington, Kentucky, USA.
Journal of applied clinical medical physics
|January 19, 2024
概括
计算机断层扫描 (CT) 辐射剂量降低至关重要. 本综述详细介绍了低剂量CT的深度学习 (DL) 方法,重点关注培训,验证和挑战.
科学领域:
- 医疗成像医学成像
- 放射学 放射学是指放射学
- 人工智能的人工智能
背景情况:
- 计算机断层扫描 (CT) 的日益使用引发了辐射剂量问题.
- 低剂量CT (LDCT) 需要有效的降噪技术.
- 深度学习 (DL) 对先进的CT图像消除有希望.
研究的目的:
- 提供对最近基于深度学习的CT拒绝方法的全面审查.
- 分析超出绩效的关键方面,包括培训,验证和通用性.
- 为突出开发强大的DL模型的挑战,用于CT图像denoising.
主要方法:
- 对CT消音算法的系统文献综述.
- 专注于低剂量CT的深度学习 (DL) 方法.
- 模型培训,验证,测试,概括性,脆弱性和评估的分析.
主要成果:
- 深度学习方法在CT图像消极化方面取得了重大进展.
- 关键的挑战包括确保模型的通用性和脆弱性评估.
- 标准化的评价指标对于比较DL无效化技术至关重要.
结论:
- 深度学习是减少CT成像中辐射剂量的强大工具.
- 需要进一步的研究来解决DL模型开发和部署的具体挑战.
- 本综述旨在指导未来的研究和临床应用基于DL的CT无声化.
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