定量双回声稳定状态T2上肢外周神经和肌肉的映射
Gracyn J Campbell1, Darryl B Sneag1, Sophie C Queler2
1Department of Radiology and Imaging, Hospital for Special Surgery, New York, NY, United States.
Frontiers in neurology
|March 1, 2024
概括
深度学习重建 (DLR) 改善了对外围神经病变评估的定量T2映射. 用DLR增强的T2地图准确地区分异常的神经和肌肉与健康组织,有助于诊断诸如外围神经病变等疾病.
科学领域:
- 医疗成像医学成像
- 神经学 神经学
- 人工智能在医学中的应用
背景情况:
- T2映射对于表征外围神经病变和肌肉缩至关重要.
- 三维双回声稳定状态 (DESS) 提供同时的3D定性信息和T2地图.
- 在DESS中信号与噪声比不够,可能会导致T2值偏差,影响定量准确性.
研究的目的:
- 评估深度学习重建 (DLR) 对DESS-T2值的影响.
- 评估使用DESS-T2地图来区分上肢神经和肌肉的异常与正常的可行性,使用电肌图作为参考标准.
主要方法:
- 分析了来自25名受试者的成像数据.
- 在肌肉和神经中使用和不使用DLR的DESS-T2值的比较.
- DESS-T2值与肌肉异常的电肌图发现的相关性.
主要成果:
- 在异常和正常肌肉中,DLR显著降低了DESS-T2值,这表明噪音减少.
- 平均DESS-T2值在异常的神经和肌肉中明显高于正常的神经和肌肉,不论DLR.
- 肌肉DESS-T2值升高与电肌图学确定的运动单元招募相关.
结论:
- 深度学习重建可以提高DESS-T2值的定量准确性.
- 定量DESS-T2映射有效地区分了受外围神经病变影响的神经和肌肉与健康组织.
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