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图像重新采样对基于放射学的人工智能在多中心前列腺MRI中的性能的影响

Jeroen Bleker¹, Christian Roest¹, Derya Yakar^1,2

¹Medical Imaging Center, Departments of Radiology, Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.

Journal of magnetic resonance imaging : JMRI

|August 12, 2023

概括

图像重新采样显著改善了使用MRI放射学在多个中心的前列腺癌检测. 优化的2D和3D重新采样策略提高了诊断性能,克服了数据异质性挑战.

科学领域:

放射学和人工智能在医学成像中的作用
前列腺癌的诊断方法前列腺癌的诊断方法
多中心MRI研究多中心MRI研究

背景情况:

前列腺癌 (PCa) 的单中心放射学模型对数据异质性表现出敏感性.
这种异质性限制了当前PCa放射学模型的诊断能力.
跨不同中心和扫描仪的标准化对于可靠的PCa检测至关重要.

研究的目的:

调查图像重新采样技术对放射学诊断性能的影响.
为了在前列腺MRI的多中心环境中评估这些影响.
确定最佳的重新采样策略,以改善PCa检测.

主要方法:

通过使用1.5T和3T扫描仪对来自9个中心的930名患者进行了回顾性分析.
使用T2加权成像 (T2W) 和扩散加权成像 (DWI) 数据.
使用各种重新采样方法 (分辨率,维度,插值) 创建了48个规范化的放射学数据集,以训练PCa检测模型.

主要成果:

最好的2D重新采样模型 (T2W:0.5mm Bspline,DWI:2mm最近邻居) 显著超过了2D基线 (AUC:0.77对比0.64).
最好的3D重新采样模型 (T2W:0.8毫米线性,DWI:2.5毫米最近邻) 显著超过了3D基线 (AUC:0.79对比0.67).
重复采样证明了对多中心放射学AI的诊断性能产生重大影响.

结论:

图像重新采样显著影响前列腺MRI中的多中心放射学AI的性能.
推的2D重新采样:同位素T2W在0.5毫米 (Bspline) 和DWI在2毫米 (最近的邻居).
推的3D放射学:同位素T2W在0.8mm (线性) 和DWI在2.5mm (最近的邻居).

关键词:

这就是为什么MRI是MRI.插值的插值是指一个插值.进行预处理.前列腺新生体前列腺瘤.监督机器学习是指监督机器学习.

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