Abstract #0910

Quantification of NOE effect and its application on brain tumor detection

Jingyi Yu¹, Jian Wu¹, Xinli Lan¹, Yonggui Yang², Zhigang Wu³, Congbo Cai¹, and Shuhui Cai¹

¹Department of Electronic Science, Xiamen University, Xiamen, China, ²Department of Radiology, The Second Affiliated Hospital of Xiamen Medical College, Xiamen, China, ³MSC Clinical & Technical Solutions, Philips Healthcare, Shenzhen, China

Synopsis

Keywords: CEST & MT, Machine Learning/Artificial IntelligenceChemical exchange saturation transfer (CEST) quantification is mostly based on pixel-wise fitting of Z-spectra, which is time-consuming and noise-sensitive. Herein, we propose an approach to quantify CEST based on U-Net. The proposed method can simultaneously quantify the concentration and the exchange rate of nuclear Overhauser enhancement (NOE), together with the B₀ map. The results of a simulation sample and a rat C6 glioma model suggest that the quantification of NOE effect with U-Net is accurate, precise and fast.

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