调查成像因素在基于CT的纹理分析指标变异性中的作用
Bino Abel Varghese1, Steven Yong Cen1, Kristin Jensen2
1Keck Medical Center, Department of Radiology, University of Southern California, Los Angeles, California, USA.
Journal of applied clinical medical physics
|November 14, 2023
概括
像标准偏差 (SD) 这样的放射性纹理特征对计算机断层扫描 (CT) 图像噪声敏感. 减少CT剂量或切片厚度会增加SD,但深度学习重建方法显示更稳定的放射性特征.
科学领域:
- 放射学 放射学是一门学科.
- 医疗成像医学成像
- 人工智能在医学中的应用
背景情况:
- 放射性纹理特征在医学成像中越来越多地用于定量分析.
- 这些特征的稳定性对于可靠的临床应用至关重要.
- 计算机断层扫描 (CT) 成像参数可以显著影响图像纹理.
研究的目的:
- 在不同的CT采集和重建参数下评估一级放射性纹理特征 (四分位数范围,变率,标准偏差) 的稳定性.
- 评估剂量降低,切片厚度和重建算法对放射性特征强度的影响.
主要方法:
- 获得了水幻,肝幻和活体人肝的CT扫描.
- 参数变化包括切片厚度 (0.625-2.5毫米),剂量 (标准,减少40%,减少60%) 和重建算法 (DLIR高,ASiR-V50%,FBP).
- 从定义的感兴趣区域提取了定量纹理特征 (IQR,CV,SD).
主要成果:
- 标准偏差,变化系数和四分位数间范围通常随着剂量和切片厚度的减少而增加.
- 深度学习图像重建 (DLIR-high) 与代重建 (ASiR-V50%) 和过后投影 (FBP) 相比,显示出更稳定的放射性特征.
- 过后投影 (FBP) 显示出更明显的波桑统计数据变化.
结论:
- 剂量减少和技术参数的图像噪声变化始终影响幻影和患者数据中的放射性特征.
- 进一步的多中心验证是必要的,以建立放射学质量保证标准.
- 深度学习重建显示了增强放射性特征稳定的前景.
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