Guo Chenlu, Wu Jian, Rosenberg Jens T, Roussel Tangi, Cai Shuhui, Cai Congbo
Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China.
The National High Magnetic Field Laboratory, Florida State University, Tallahassee, FL, USA.
Magn Reson Med. 2020 Dec;84(6):3192-3205. doi: 10.1002/mrm.28376. Epub 2020 Jun 30.
To develop a method for fast chemical exchange saturation transfer (CEST) imaging.
The periodically rotated overlapping parallel lines enhanced reconstruction (PROPELLER) sampling scheme was introduced to shorten the acquisition time. Deep neural network was employed to reconstruct CEST contrast images. Numerical simulation and experiments on a creatine phantom, hen egg, and in vivo tumor rat brain were performed to test the feasibility of this method.
The results from numerical simulation and experiments show that there is no significant difference between reference images and CEST-PROPELLER reconstructed images under an acceleration factor of 8.
Although the deep neural network is trained entirely on synthesized data, it works well on reconstructing experimental data. The proof of concept study demonstrates that the combination of the PROPELLER sampling scheme and the deep neural network enables considerable acceleration of saturated image acquisition and may find applications in CEST MRI.
开发一种快速化学交换饱和转移（CEST）成像方法。
引入周期性旋转重叠平行线增强重建（PROPELLER）采样方案以缩短采集时间。采用深度神经网络重建CEST对比图像。对肌酸模型、鸡蛋和体内肿瘤大鼠脑进行了数值模拟和实验，以测试该方法的可行性。
数值模拟和实验结果表明，在加速因子为8的情况下，参考图像与CEST-PROPELLER重建图像之间无显著差异。
虽然深度神经网络完全基于合成数据进行训练，但在重建实验数据方面表现良好。概念验证研究表明，PROPELLER采样方案与深度神经网络相结合能够显著加速饱和图像采集，可能在CEST MRI中得到应用。
