Deep neural network based CEST and AREX processing: Application in imaging a model of Alzheimer’s disease at 3 T
Jianpan Huang, Joseph H. C. Lai, Kai-Hei Tse, Gerald W. Y. Cheng, Yang Liu, Zilin Chen, Xiongqi Han, Lin Chen, Jiadi Xu, Kannie W. Y. Chan
Abstract
Purpose
To optimize and apply deep neural network based CEST (deepCEST) and apparent exchange dependent-relaxation (deepAREX) for imaging the mouse brain with Alzheimer’s disease (AD) at 3T MRI.
Methods
CEST and T1 data of central and anterior brain slices of 10 AD mice and 10 age-matched wild type (WT) mice were acquired at a 3T animal MRI scanner. The networks of deepCEST/deepAREX were optimized and trained on the WT data. The CEST/AREX contrasts of AD and WT mice predicted by the networks were analyzed and further validated by immunohistochemistry.
Results
After optimization and training on CEST data of WT mice, deepCEST/deepAREX could rapidly (~1 s) generate precise CEST and AREX results for unseen CEST data of AD mice, indicating the accuracy and generalization of the networks. Significant lower amide weighted (3.5 ppm) signal related to amyloid β-peptide (Aβ) plaque depositions, which was validated by immunohistochemistry results, was detected in both central and anterior brain slices of AD mice compared to WT mice. Decreased magnetization transfer (MT) signal was also found in AD mice especially in the anterior slice.
Conclusion
DeepCEST/deepAREX could rapidly generate accurate CEST/AREX contrasts in animal study. The well-optimized deepCEST/deepAREX have potential for AD differentiation at 3T MRI.