开发和评估一个深度学习模型,用于多频的吉布斯文物消除
Lisong Dai1, Dan Wang1, Xin Mao2
1Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
一个新的深度学习模型有效地从MRI扫描中删除了Gibbs文物,提高了图像质量,并有助于诊断. 这种先进的技术提高了医学成像的诊断信心和准确性.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 放射学 放射学是一门学科.
背景情况:
- 吉布斯工件是由k空间截断引起的,降低了MRI图像质量.
- 这些文物可能会被误认为是syrinx,使诊断复杂化.
- 目前用于移除文物的方法是不够的.
研究的目的:
- 开发和验证深度学习 (DL) 模型,用于多频率的吉布斯文物消除.
- 评估模型在不同解剖区域,MRI序列和文物严重性的表现.
- 评估文物移除对Syrinx诊断的影响.
主要方法:
- 对290,940张来自4,936次扫描的MRI图像进行了回顾性分析.
- 用人工生成的吉布斯文物训练DL模型.
- 基于20名健康成年人和10名鼻患者的数据进行了前性验证.
主要成果:
- 处理后的图像显示质量明显高于原始图像或传统过图像 (P<0.05).
- 随着DL模型的使用,Syrinx识别的信心增加了 (AUC:0.95对比0.90,P=0.04).
- 该模型在各种条件下展示了强大的吉布斯文物移除.
结论:
- DL模型有效地消除了吉布斯的文物.
- 它显示了提高 syrinx 识别准确性的潜力.
- 该模型是强大的,并在现实世界的临床场景中表现良好.
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