2DeteCT - 一个大型的2D可扩展,可训练,实验性的计算机断层学数据集,用于机器学习
Maximilian B Kiss1, Sophia B Coban2,3, K Joost Batenburg2,4
1Centrum Wiskunde & Informatica, Computational Imaging group, Amsterdam, 1098 XG, The Netherlands. maximilian.kiss@cwi.nl.
Scientific data
|September 4, 2023
概括
这项研究引入了一个新的,开放的2D风扇束X射线计算机断层扫描 (CT) 数据集. 本资源支持用于图像重建的机器学习 (ML) 开发,解决计算成像中的实验数据稀缺问题.
科学领域:
- 计算机成像成像技术
- 医学成像医学成像
- 机器学习应用程序 机器学习应用程序
背景情况:
- 图像重建的机器学习 (ML) 需要大量的测量数据集和基准真实图像.
- 对X射线计算机断层扫描 (CT) 的实验数据集有限,这阻碍了ML方法的开发.
- 目前的方法通常依赖于模拟数据,缺乏现实世界的实验验证.
研究的目的:
- 为研究界提供一个多功能,开放的2D风扇束CT数据集.
- 促进ML技术的开发和评估,用于各种图像重建任务.
- 为弥补基于ML的CT成像实验数据的差距.
主要方法:
- 设计了一种半自动扫描程序,使用灵活的实验室X射线CT设置.
- 在高分辨率下采集了5000个切片,样本多样化 (形状和密度不同).
- 收集了三种光束特征 (高保真度,低剂量,光束硬化) 和750个分布外切片的数据.
主要成果:
- 生成了一个全面的2D风扇光束CT数据集与原始投影数据.
- 从开源处理管道中获得的提供参考重建和细分.
- 数据集包括稳定性和细分任务的变化.
结论:
- 开放的数据集使得在X射线CT图像重建方面进行先进的ML研究.
- 促进开发更强大,更准确的CT成像技术.
- 支持使用现实的实验数据验证ML算法.
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