从高b值扩散权重乳腺MRI进行虚拟对比增强的最大强度预测:一项可行性研究
Andrzej Liebert1, Hannes Schreiter2, Dominique Hadler2
1Institute of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany. andrzej.liebert@uk-erlangen.de.
European radiology experimental
|October 8, 2025
概括
人工智能生成的虚拟对比增强 (VAbCE) 乳房MRI显示了病变检测的前景,与未增强的图像相比,改善了评分器之间的一致性和灵敏度. 这种方法为乳腺癌查应用提供了潜在的新方法.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 最大强度预测 (MIPs) 对于快速检测乳房MRI中的病变至关重要,使用对比度增强 (CE) 和扩散权重成像 (DWI).
- 评估基于人工智能的虚拟CE减法MIP作为可行的阅读方法,对于提高诊断效率至关重要.
研究的目的:
- 评估人工智能生成的虚拟缩短对比增强 (VAbCE) 减法MIPs用于乳腺MRI病变检测的可行性.
- 为了比较VAbCE MIPs的诊断性能,图像质量和文物存在,与未增强的 (UnE) 和缩写的CE (AbCE) MIPs相比.
主要方法:
- 一项对540个多参数乳腺MRI检查 (2017-2020年) 进行的回顾性研究,其中多个b值DWI.
- 训练了一个2D U-Net模型,从UnE图像中生成VAbCE减法.
- 两位放射科医生评估了UnE,VAbCE和AbCE图像的损伤怀疑,图像质量,文物和损伤明显性.
主要成果:
- 癌症检测率为90.0% (UnE),91.4% (VAbCE) 和94.3% (AbCE) 的癌症检测率.
- VAbCE MIPs显示了比UnE (0.39) 的更高的评价者间一致性 (Cohen κ=0.53),与AbCE (0.58) 相比.
- 在VAbCE和AbCE之间没有发现明显性,质量或阅读时间的显著差异;VAbCE的文物较少.
结论:
- 基于AI的VAbCE乳房MRI增强了测量器之间的一致性,并且比UNE图像提供了略有提高的灵敏度,接近AbCE灵敏度.
- 来自神经网络的 VAbCE MIP 能够快速进行视觉评估,这表明选应用的潜力.
- 需要进一步的研究,以充分探索VAbCE乳房MRI的诊断能力.
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