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图像质量和标准和深度学习重建的T2加权脊柱MRI之间的可互换性进行比较分析

Seungeun Lee¹, Joon-Yong Jung¹, Heeyoung Chung¹

¹Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea.

Magnetic resonance imaging

|March 21, 2024

概括

深度学习 (DL) 重建的T2加权图像 (T2-WI) 与传统的T2-WI提供可比的图像质量和诊断性能,用于脊柱成像. 这种先进的方法显著减少了MRI采集时间,提高了临床环境中的效率.

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
脊柱成像技术脊柱成像技术

背景情况:

加速磁共振成像 (MRI) 使用深度学习 (DL) 来重建低样本数据.
传统的T2加权成像 (T2-WI) 是脊柱评估的标准.
对DL重建的T2-WI与传统的T2-WI进行评估对于临床采用至关重要.

研究的目的:

为了比较DL重建的T2-WI与传统的T2-WI的图像质量.
评估DL重建的T2-WI在检测退行性脊柱病变的诊断性能.
评估DL重建对MRI采集时间的影响.

主要方法:

62名患者接受了C-脊柱和L-脊柱MRI,使用传统和DL重建的T2-WI.
通过非均度测量和结构可见度分级来评估图像质量.
三位放射科医生独立评估了退行性病变;使用可互换性分析测试了诊断等价性.

主要成果:

与传统扫描相比,DL重建的T2-WI采集时间减少了21-58%.
图像质量是可比的,DL T2-WI显示同等或更高的结构可见性,特别是对于特定的脊柱结构.
诊断性能是相当的,协议之间病变检测的一致率差异<5%.

结论:

深度学习重建为T2-WI脊柱成像提供了可比的图像质量和诊断性能.
经DL重建的T2-WI显著减少了MRI扫描时间.
DL重建是加速脊柱MRI的一种可行的技术.

关键词:

加速成像图像处理器深度学习-重建的重建降低噪音减少噪音脊椎的MRI是指脊椎的MRI.

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