图像重建方法对肺肺复发症诊断的影响 卷积神经网络的肺肺复发症
Toshiki Takeshita1, Atsushi Nambu2, Masao Tago3
1Department of Radiology, Teikyo University Hospital, Mizonokuchi, 5-1-1 Futago, Takatsu-ku, Kawasaki, Kanagawa, 213-8507, Japan. t.takeshita.1227@gmail.com.
在CT成像中,代重建 (IR) 方法并没有改善卷积神经网络 (CNN) 性能,以诊断肺气. 在过后投影 (FBP) 图像上训练的模型表现优于在IR图像上训练的模型,这表明FBP更适合这项诊断任务.
科学领域:
- 放射学 放射学是一门学科.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 计算机断层扫描 (CT) 对于诊断肺气至关重要.
- 代重建 (IR) 算法旨在减少CT扫描中的图像噪声.
- 卷积神经网络 (CNN) 显示出从医学图像中自动诊断疾病的前景.
研究的目的:
- 研究不同代重建 (IR) 方法对CT图像质量的影响.
- 评估这些IR方法如何影响CNN模型在诊断肺复发症的表现.
- 为了确定基于CNN的肺瘤诊断的最佳CT重建方法.
主要方法:
- 对97名患者CT数据集的回顾性分析.
- 使用过后投影 (FBP) 和自适应统计代重建V (ASIR-V) 在不同混合水平 (30%,50%,70%) 的CT数据的重建.
- 使用重建图像进行CNN模型 (FBP,ASIR-V30,ASIR-V50,ASIR-V70) 的培训和评估.
主要成果:
- 用更高的ASIR-V混合水平重建的CT图像显示噪声减少 (较低的标准偏差).
- 与在IR图像上训练的模型相比,在FBP图像上训练的CNN模型在诊断肺复发症方面表现明显更好.
- 当FBP训练模型应用于ASIR-V70测试图像时,获得了最佳的诊断性能,而ASIR-V70模型在FBP测试图像上表现最差.
结论:
- 与自适应统计代重建V (ASIR-V) 相比,过后投影 (FBP) 重建在基于CNN的肺复发症诊断中产生了更高的性能.
- 尽管降低了噪声,但IR算法并没有提高,甚至可能降低了CNN肺气的诊断准确性.
- 这些发现表明,传统的FBP重建可能更适合为这种特定的临床应用培训CNN.
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