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Abstract #3008

Non-invasive quantitative estimation of blood oxygen saturation with MRI: feasibility of machine learning

Juliet Varghese1, Rizwan Ahmad1,2, Subha Raman1,3,4, Lee C Potter5, and Orlando P Simonetti1,3,4

1Dorothy M. Davis Heart and Lung Research Institute, The Ohio State University, Columbus, OH, United States, 2Department of Biomedical Engineering, The Ohio State University, Columbus, OH, United States, 3Division of Cardiovascular Medicine, Department of Internal Medicine, The Ohio State University, Columbus, OH, United States, 4Department of Radiology, The Ohio State University, Columbus, OH, United States, 5Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH, United States

Non-invasive estimation of intra-cardiac blood oxygen (O2) saturation by magnetic resonance (MR) imaging would be useful in evaluating shunt severity in congenital heart disease, and oxygen delivery and consumption energetics in heart failure and pulmonary hypertension. Accurate estimation of blood O2 saturation from MR data may be limited, however, by the lack of an accurate model to characterize the dependence on T2 relaxation of blood on its O2 saturation level. The present study explores the feasibility of machine learning to accurately predict blood O2 saturation; the performance is evaluated in a preliminary cohort of patients against the Luz-Meiboom model.

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