在施用对比剂之前,在对比度增强的乳房MRI中智能预测器件
Andrzej Liebert1, Badhan K Das2, Lorenz A Kapsner2,3
1Institute of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany. andrzej.liebert@uk-erlangen.de.
European radiology
|December 15, 2023
概括
神经网络可以在注射对比剂之前预测对比度增强的乳房MRI扫描中的潜在工件. 这种预测能力可能有助于防止未来乳腺MRI检查中的图像工件.
科学领域:
- 医疗成像医学成像
- 放射学中的人工智能
- 乳房MRI分析 分析
背景情况:
- 对比度增强 (CE) 乳腺MRI最大强度预测 (MIPs) 中的人工物可以影响诊断准确性.
- 在使用基于加多的对比剂 (GBCA) 之前预测这些工件对于改善图像质量至关重要.
研究的目的:
- 评估使用前对比T1加权图像对CE乳房MRIMIP预测文物的可行性.
- 评估神经网络在GBCA注射之前预测这些工件的性能.
主要方法:
- 在四个MRI装置中对2884次乳房CE MRI检查进行了回顾性分析.
- 一个由五个DenseNet模型组成的整体分类器 (EC) 在对比前T1加权图像上进行训练,以预测对比后MIP中的工件.
- 读者评估和诊断准确度指标 (AuROC) 用于评估独立测试集 (n=285) 的性能.
主要成果:
- 在所有分析的乳房MRI检查中,在53.6%的乳房中检测到潜在的重要文物.
- 在GBCA注射之前,EC在预测人工物方面实现了89%的特异性和31%的灵敏性,AuROC为0.66.
- 这项研究表明,神经网络能够在对比注射之前预测CE乳房MRI器件.
结论:
- 神经网络可以预测CE乳房MRI减去数据中的工件发生在GBCA管理之前.
- 这种预测能力可能使个性化,在扫描算法,以防止乳房MRI文物.
- 需要进一步的研究来确认这些预测成像方法的临床实用性.
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