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深度学习的诊断评估加速的腰椎脊椎MRI
Komal M Awan1,2, Augusto Lio M Goncalves Filho3, Azadeh Tabari1,2
1Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, USA.
The neuroradiology journal
|January 9, 2024
概括
腰部MRI的深度学习 (DL) 重建显著减少扫描时间,而不会影响像狭窄症这样的疾病的诊断质量. 这种先进的技术保持了与传统MRI方法相匹配的诊断性能.
科学领域:
- 放射学和医学成像学 医学成像学
- 医疗保健中的人工智能
- 深度学习应用程序
背景情况:
- 深度学习 (DL) 加快了磁共振成像 (MRI) 检查,但其对全腰椎MRI协议的影响尚未得到充分探索.
- 现有的研究重点是针对特定的腰部MRI序列的DL加速,在了解其对常规协议的影响方面存在差距.
研究的目的:
- 与传统协议相比,评估DL加速腰椎脊柱MRI协议的诊断质量和性能.
- 评估完全基于DL的MRI协议对常规腰椎MRI扫描时间和诊断实用性的影响.
主要方法:
- 对36名门诊患者进行非对比的腰椎脊柱MRI的前性评估,使用常规和DL加速协议.
- 在这两种协议中,包括了形T1,T2,STIR和轴向T2加权序列.
- 两个失明的神经放射学家对狭窄和关节病进行独立审查;对图像质量,SNR和文物进行利克尔特尺度评估;威尔科克森签署了等级测试以进行分级比较.
主要成果:
- 在评估甲状腺狭窄,脊髓管狭窄,神经根压缩或面部关节病的方案之间没有显著差异 (p > .05).
- 通过DL加速方案显示了整体诊断质量和关键解剖结构 (绳索,脑脊液,圆盘,神经根) 的可视化.
- 通过DL加速方案,发现信号噪声比 (SNR) 降低和人工物感知增加;观察者间可重复性中度至实质性 (κ = 0.50-0.76).
结论:
- 脊柱成像中的深度学习重建有效地减少了MRI采集时间.
- 在使用DL重建时,与用于腰椎评估的传统MRI技术相比,保持了可比的诊断质量.
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