3708
CEST imaging with neural network fitting of the human brain at 3T
Zhichao Wang1, Yu Zhao2, Xu Yan3, Zhongshuai Zhang3, Caixia Fu3, Hui Tang4, and Jianqi Li1
1Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, China, 2Institute of Imaging Science, Vanderbilt University, Nashville, TN, United States, 3MR Collaboration NE Asia, Siemens Healthcare, Shanghai, China, 4Department of Radiology, Renji Hospital affiliated to Shanghai Jiao Tong University Medical College, Shanghai, China
 Separating different targets is highly valuable for clinical application of CEST. In this study, the background Z-spectra including only the magnetization transfer and direct saturation effects was fitted by using neural network, then CEST and NOE maps were obtained simultaneously. 
FIGURE 1 Schematic of data processing pipeline. Simulated background Z-spectrum (A) are generated. In each simulated background Z-spectrum (C), the data marked as red solid dots are inputted for training and the data in blue line are target for training. (D)The feedforward neural network. (B) The data marked as red solid dots from the acquired Z-spectrum are inputted for prediction. (E) The background Z-spectrum (marked as dashed blue curve) is obtained from the network. (F)The APT map and NOE map are obtained by subtracting background Z-spectrum from the acquired Z-spectrum.
FIGURE 3 The results from a patient with cerebral infarction. The 1st row includes the conventional T1-weighted image (A), T2-weighted FLAIR image (B), APT map (C) and NOE map (D). The 2nd row includes the fitting Z- spectrum of infarction tissue (E) and normal tissue (F), boxplots of APT contrast (G) and NOE contrast (H) between infarction and normal tissues. In (E) and (F), the red curves are B0-corrected Z- spectra, and the blue lines are background Z- spectra from neural network fitting. The red arrow in the T2W FLAIR image indicates the area of the lesion.