tDKI-Net:一个联合的q-t空间学习网络,用于扩散时间依赖的Kurtosis成像
IEEE journal of biomedical and health informatics
|June 21, 2024
概括
时间依赖扩散MRI (TDDMRI) 模型需要长时间的扫描时间. 我们的tDKI-Net模型通过使用降低样本数据来准确估计组织微观结构和跨膜交换来加速TDDMRI,从而减少了10.5倍的扫描时间.
科学领域:
- 磁共振成像技术 磁共振成像技术
- 生物物理学的生物物理.
- 计算生物学 计算生物学
背景情况:
- 时间依赖的扩散磁共振成像 (TDDMRI) 能够进行非侵入性的组织微观结构特征.
- 当前的TDDMRI模型需要密集采样的q-t空间数据,这导致了漫长的采集协议.
- 加快TDDMRI获取对于临床适用性和更广泛的研究至关重要.
研究的目的:
- 开发和验证tDKI-Net,这是一个新的深度学习模型,用于估计使用低采样的q-t空间数据的扩散时间依赖的曲解和跨膜交换.
- 为了显著减少TDDMRI扫描时间,而不会牺牲估计准确性.
- 提高生物组织中微观结构表征的效率.
主要方法:
- 拟议的tDKI-Net,一个联合的q-t空间模型,集成q-Encoders和基于外梯度机制的t-Encoder.
- 采用了三阶段的培训策略:以物理为基础的自我监督预训,DKI热身和联合培训.
- 利用下方采样的q-t空间数据进行模型训练和验证,与现有方法比较性能.
主要成果:
- tDKI-Net从低样本数据有效估计了依赖于扩散时间的皮质分裂和跨膜交换时间.
- 拟议的三阶段培训策略超过了从头开始的培训,减少了NRMSE的高达1.4%.
- 与之前的三主体培训研究相比,在一个主体培训中,为 , , 和 (2.50%, 3.04%, 10.86%) 实现了较低的NRMSEs.
- 通过联合 q-t 空间数据减样,减少了 10.5 倍的扫描时间,而不会影响准确度.
结论:
- tDKI-Net提供了TDDMRI采集协议的显著加速.
- 该模型准确估计了关键的微结构参数,包括跨膜交换时间.
- 这种方法有望实现基于MRI的更有效和更容易获得的扩散组织特征.
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