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DEISM:深度重建框架与加速化学交换和转移成像的自我校准机制

Jianping Xu, Tao Zu, Yi-Cheng Hsu

IEEE transactions on bio-medical engineering

|March 4, 2025

概括

加快化学交换和转移 (CEST) 成像是通过一种新的深度学习框架 (DEISM) 实现的. 这种方法使用文物信息进行重建,显著提高图像质量和减少扫描时间.

科学领域:

磁共振成像技术磁共振成像技术
医疗成像医学成像
生物医学工程生物医学工程

背景情况:

化学交换和转移 (CEST) 图像提供了有价值的分子信息,但由于多次数据采集,扫描时间延长.
加速成像技术对于克服CESTMRI中长时间采集时间的局限性至关重要.
利用文物信息作为图像重建的先验是加速CEST成像中的未经探索的领域.

研究的目的:

开发和验证一种具有自我校准机制 (DEISM) 的新型深度重建框架,用于高度加速的CEST成像.
调查文物信息的实用性,特别是空间频率冗余性,作为CEST图像重建的先验.
在加速采集中提高CEST图像,分子地图和光谱的质量.

主要方法:

提出了一个新的深度重建框架 (DEISM),集成一个用于初始重建的基于模型的网络和一个数据驱动的文物抑制 (AS) 网络.
开发了一种独特的编码器-解码器架构,具有多尺度特征融合,用于强大的文物估计和校正.
通过模拟数据进行DEISM框架端到端的训练,并对健康志愿者和脑瘤患者的表现进行了评估,其加速因子有所变化.

主要成果:

在加速的CEST成像中展示了数据驱动的文物抑制 (AS) 概念的可行性.
展示了利用文物场内的空间频率相关性的有效性,以改进重建.
DEISM提供了高质量的源图像,分子地图和CEST光谱,性能优于传统和最先进的方法.

结论:

通过利用文物信息作为强大的优先权,DEISM框架成功地加速了CEST成像.
将先前的图像文物整合到基于学习的重建中,可以显著提高CEST的成像质量和诊断潜力.
DEISM提供了一个有前途的解决方案,可以在不影响图像保真性的情况下减少CEST MRI的扫描时间.

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