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Abstract #3224

Sparsity based machine learning algorithms for oxygen extraction fraction mapping

Junghun Cho1, Hae-Yeoun Lee2, jinwei Zhang1, Pascal Spincemaille3, Hang Zhang4, Simon Hubertus5, Yan Wen1, Ramin Jafari1, Shun Zhang3, Thanh Nguyen3, Ajay Gupta3, and Yi Wang1,3
1Biomedical Engineering, Cornell University, New York, NY, United States, 2Computer Software Engineering, Kumoh National Institute of Technology, Gumi, Republic of Korea, 3Radiology, Weill Cornell Medical College, New York, NY, United States, 4Electrical and Computer Engineering, Cornell University, New York, NY, United States, 5Computer Assisted Clinical Medince, Heidelberg University, Mannheim, Germany

In this work, dictionary and deep learning based algorithms are developed that take advantage of sparse signal representations to improve the accuracy and speed of oxygen extraction fraction (OEF) mapping based on the QSM+qBOLD (QQ) modeling of multi-echo gradient echo data without vascular challenge. The developed dictionary learning (QQ-DL) and deep neural network (QQ-NET) algorithms are significantly faster and provide more accurate OEF maps in simulation than a current algorithm based on cluster analysis of time evolution (CAT). In ischemic stroke patients, QQ-DL and QQ-NET show OEF maps that are consistent with DWI-defined lesions.

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