Abstract #5038

Synthetic data driven learning for full-automatic hemoperfusion parameter estimation

Lu Wang¹, Pujie Zhang¹, Zhen Xing², Congbo Cai¹, Zhong Chen¹, Dairong Cao², Zhigang Wu³, and Shuhui Cai¹

¹Department of Electronic Science, Xiamen University, Xiamen, China, ²Department of Radiology, First Affiliated Hospital of Fujian Medical University, Fuzhou, China, ³MSC Clinical & Technical Solutions, Philips Healthcare, Shenzhen, China

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Brain, Hemoperfusion parameter estimationHemoperfusion magnetic resonance (MR) imaging derived parameters characterize both endothelial hyperplasia and neovascularization that are associated with tumor aggressiveness and growth. However, the hemoperfusion parameter estimation is still limited by low reliability, high bias, long processing time, and operator experience dependency up to now. In this study, a synthetic data driven learning method for hemoperfusion parameter estimation is proposed. Image analysis shows that the proposed method improves the reliability and precision of hemodynamic parameter estimation in a full-automatic and high-efficient way.

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