Wenbo Li1,2, Ksenija Grgac1,2, Alan Huang1,2,3, Nirbhay Yadav1,2, Qin Qin1,2, Peter C M van Zijl1,2. 1. Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. 2. F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA. 3. Philips Healthcare, Best, The Netherlands.
Abstract
PURPOSE: To propose and evaluate a model for the blood water T1 that takes into account the effects of hematocrit fraction, oxygenation fraction, erythrocyte hemoglobin concentration, methemoglobin fraction, and plasma albumin concentration. METHODS: Whole blood and lysed blood T1 data were acquired at magnetic fields of 3 Tesla (T), 7T, 9.4T, and 11.7T using inversion-recovery measurements and a home-built blood circulation system for maintaining physiological conditions. A quantitative model was derived based on multivariable fitting of this data. RESULTS: Fitting of the model to the data allowed determination of the different parameters describing the blood water T1 such as those for the diamagnetic and paramagnetic effects of albumin and hemoglobin, and the contribution of methemoglobin. The model correctly predicts blood T1 at multiple fields, as verified by comparison with existing literature. CONCLUSION: The model provides physical and physiological parameters describing the effects of hematocrit fraction, oxygenation, hemoglobin concentration, methemoglobin fraction, and albumin concentration on blood water T1 . It can be used to predict blood T1 at multiple fields. Magn Reson Med 76:270-281, 2016.
PURPOSE: To propose and evaluate a model for the blood water T1 that takes into account the effects of hematocrit fraction, oxygenation fraction, erythrocyte hemoglobin concentration, methemoglobin fraction, and plasma albumin concentration. METHODS: Whole blood and lysed blood T1 data were acquired at magnetic fields of 3 Tesla (T), 7T, 9.4T, and 11.7T using inversion-recovery measurements and a home-built blood circulation system for maintaining physiological conditions. A quantitative model was derived based on multivariable fitting of this data. RESULTS: Fitting of the model to the data allowed determination of the different parameters describing the blood water T1 such as those for the diamagnetic and paramagnetic effects of albumin and hemoglobin, and the contribution of methemoglobin. The model correctly predicts blood T1 at multiple fields, as verified by comparison with existing literature. CONCLUSION: The model provides physical and physiological parameters describing the effects of hematocrit fraction, oxygenation, hemoglobin concentration, methemoglobin fraction, and albumin concentration on blood water T1 . It can be used to predict blood T1 at multiple fields. Magn Reson Med 76:270-281, 2016.
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