Jianpan Huang1, Joseph H C Lai1, Kai-Hei Tse2, Gerald W Y Cheng2, Yang Liu1,3, Zilin Chen1, Xiongqi Han1, Lin Chen4,5,6, Jiadi Xu4,5, Kannie W Y Chan1,3,5,7. 1. Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China. 2. Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China. 3. Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China. 4. F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, Maryland, USA. 5. Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. 6. Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China. 7. City University of Hong Kong Shenzhen Research Institute, Shenzhen, China.
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.
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.
Authors: Karl Ludger Radke; Lena Marie Wilms; Miriam Frenken; Julia Stabinska; Marek Knet; Benedikt Kamp; Thomas Andreas Thiel; Timm Joachim Filler; Sven Nebelung; Gerald Antoch; Daniel Benjamin Abrar; Hans-Jörg Wittsack; Anja Müller-Lutz Journal: Int J Mol Sci Date: 2022-06-22 Impact factor: 6.208