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通过最佳频率偏移选择和深度学习重建进行生物分子成像

Adarsha Bhattarai¹, Chathumi Samaraweera¹, Mariano Uberti²

¹Department of Electrical and Computer Engineering, University of Nebraska-Lincoln, Omaha, NE 68182, USA.

IEEE access : practical innovations, open solutions

|August 29, 2025

概括

加快化学交换和转移 (CEST) MRI 使用人工智能从稀疏的频率偏移重建数据,使得扫描速度快10倍. 这种进步提高了生物医学研究和临床应用的分子成像.

科学领域:

生物医学工程
医学成像
医学的人工智能

背景情况:

化学交换和转移 (CEST) 磁共振成像 (MRI) 是一个有前途的非侵入性分子成像技术.
由于需要多个频率偏移,目前的CEST MRI方法需要长时间的数据采集.
加快CESTMRI数据采集对于推进临床前研究和临床应用至关重要.

研究的目的:

开发一种加速CESTMRI数据采集的方法.
用CESTMRI提高分子成像的效率.
尽管采集时间缩短,但仍保持高质量的CESTMRI数据.

主要方法:

采用了两步的方法:用于稀疏频率偏移选择的优化和用于Z频谱重建的深度学习.
一个优化算法确定了一组最优的稀疏频率偏移来收集数据.
一个深度学习算法从获得的低分辨率数据中重建了高分辨率的CEST MRI Z光谱.

主要成果:

优化技术将数据收集减少到总频率偏移点的10%.
深度学习准确地重建了密集的Z光谱,具有较低的RMSE (平均). 0.0094) 和MAE.
这种结合方法在保持数据质量的同时实现了CESTMRI采集的10倍加快.

结论:

拟议的方法通过最佳稀疏采样和深度学习重建,显著加快了CESTMRI数据采集.
这种加速的CEST MRI方法保持了高数据质量,扩大了其在生物医学研究和临床环境中的实用性.
这种技术有助于在基础科学和临床研究中推进分子生物成像.

关键词:

生物医学信号处理人工智能生物医学成像计算机视觉图像重建机器学习磁共振成像分子成像

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