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CEST和核Overhauser增强成像与深度学习-外推半固体磁化转移参考:扫描-重新扫描可复制性和可靠性研究
Hye-Young Heo1, Munendra Singh1, Vivek Yedavalli1
1The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, Maryland, USA.
Magnetic resonance in medicine
|November 27, 2023
概括
一个新的深度学习框架,DeepEMR,提供快速可靠的磁化转移对比度 (MTC) 和化学交换和转移 (CEST) 信号估计. 这种方法在健康志愿者中显示出MTC成像的高准确性和可重复性,并且在患者中改善了瘤对比度.
科学领域:
- 磁共振成像 (MRI) 是一种磁共振成像技术.
- 医疗成像中的人工智能
- 生物物理学的生物物理.
背景情况:
- 磁化转移对比 (MTC) 和化学交换和转移 (CEST) 是特征组织的关键MRI技术.
- 准确有效地估计MTC和CEST信号对于临床应用至关重要.
- 现有的信号估计方法可能是计算密集的,可能缺乏可重现性.
研究的目的:
- 开发和验证一个新的MR物理驱动的深度学习框架,名为DeepEMR.
- 提供MTC和CEST信号的快速和可靠估计.
- 在各种成像场景中评估DeepEMR估计的可重现性和可靠性.
主要方法:
- 一个神经网络被设计用于使用Z频谱特征预测水和MTC主导的信号.
- 在3T时,DeepEMR框架在数值幻象和健康志愿者身上进行了评估.
- 该方法应用于脑瘤患者,将组织对比度与传统指标进行比较.
主要成果:
- 对于APT和rNOE成像,DeepEMR准确地估计了MTC信号的±3.5ppm,具有显著的计算效率 (约. 这是190倍的).
- 在MTC信号估计方面实现了高的可重复性和可靠性 (ICC=0.97,主体间CV=3.5%,主体内CV=1.3%).
- 与传统方法相比,瘤患者的基于DeepEMR的胺质子转移图像显示出优异的瘤对比度和划分.
结论:
- 深度EMR方法是衡量清洁APT和rNOE效应的可行方法.
- 该框架显示了低的扫描-重新扫描可变性,使其适合纵向和横截面研究.
- 深度EMR为临床环境中的定量MRI分析提供了有前途的进步.
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