基于MRI的正常大脑旋转模式的自动量化和多微症和无脑症胎儿的变化,基于MRI
Bossmat Yehuda1,2, Aviad Rabinowich1,3,4, Daphna Link-Sourani1
1From the Sagol Brain Institute (B.Y., A.R., D.L.-S., N.A., O.B.-Z., D.B.B.), Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.
AJNR. American journal of neuroradiology
|December 5, 2023
概括
这项研究开发了一种自动管道,用二维MRI成像来量化胎儿大脑转化. 这种方法可以准确地识别出患有脑和多微的胎儿,有助于早期诊断皮质形.
科学领域:
- 神经成像是一种神经成像.
- 胎儿医学 胎儿医学
- 发展生物学 发展生物学
背景情况:
- 目前胎儿大脑转化评估是定性和主观的.
- 现有的定量方法需要复杂的3DMRI成像数据,并且耗时.
研究的目的:
- 开发一个自动管道用于胎儿大脑转化评估,使用常规的二维MRI成像.
- 为了量化与妊娠年龄的正常旋转变化.
- 为了从对照组中区分患有lisencephaly和polymicrogyria的胎儿.
主要方法:
- 追溯分析了162个胎儿冠状T2加权的MRI成像数据集.
- 大脑细分和计算每半球5个旋转参数.
- 评估7个机器学习分类器用于诊断差异化.
主要成果:
- 所有的转化参数都显示出随着对照群的妊娠年龄的显著变化.
- 患有lisencephaly和polymicrogyria的胎儿表现出显著减少的化.
- 机器学习模型在区分受影响的胎儿方面取得了很高的准确性 (AUC高达0.90).
结论:
- 成功开发了一种用于胎儿大脑旋转量化的自动管道.
- 该方法提供正常的发育曲线,并区分皮质形.
- 这种工具可以增强放射性评估,并促进早期检测胎儿大脑异常.
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