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深度学习能够基于临床上可行的扩散磁共振成像技术进行准确的脑组织微观结构分析

Yuxing Li¹, Zhizheng Zhuo², Chenghao Liu¹

¹School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China.

|September 24, 2024

概括

深度学习 (DL) 从简短的扩散MRI扫描中准确地重建大脑组织的微观结构. 这种方法可靠地检测疾病和与年龄相关的变化,使先进的大脑分析在临床上可行.

科学领域:

神经成像是一种神经成像.
生物医学工程生物医学工程
计算神经科学是一种神经科学.

背景情况:

扩散磁共振成像 (dMRI) 能够进行非侵入性脑组织微观结构评估.
目前基于模型的方法需要广泛的扩散梯度,由于时间限制,限制了临床应用.
深度学习 (DL) 显示了使用临床可行的dMRI进行微结构重建的潜力.

研究的目的:

通过使用临床可行的dMRI来评估DL对脑组织微观结构分析的可靠性.
为了确定DL是否保留了微妙的,与疾病或年龄相关的微观结构变化.
评估基于DL的微结构重建的临床实用性.

主要方法:

从四个大脑dMRI数据集中重建了组织微观结构,仅使用12个扩散梯度.
应用神经元定向分散和密度成像 (NODDI) 和球形平均技术 (SMT) 模型.
根据临床可行数据的既定模型对验证的DL重建.

主要成果:

DL方法精确识别了与疾病相关的和年龄相关的脑组织微观结构的变化.
使用DL从有限的扩散梯度 (12) 重建可靠的微观结构指标.
基于DL的分析显示了不同dMRI数据集的一致性.

结论:

基于DL的组织微观结构重建是临床可行的dMRI的可靠方法.
DL方法可以准确量化与疾病和衰老相关的微妙微观结构变化.
这种方法提高了先进的大脑组织分析的临床可行性.

关键词:

临床大脑分析临床大脑分析深度学习是一种深度学习.扩散磁共振成像技术的研究.组织微观结构的重建组织的重建.

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