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Abstract #1022

Segmentation of Brain Tissues using a Bayesian Estimation of Multicomponent Relaxation Values in Magnetic Resonance Fingerprinting

Debra McGivney1, Yun Jiang1, Dan Ma1, Chaitra Badve2, Vikas Gulani1, and Mark Griswold1

1Radiology, Case Western Reserve University, Cleveland, OH, United States, 2Radiology, University Hospitals, Cleveland, OH, United States

A Bayesian methodology has been previously applied to MRF reconstructions to analyze subvoxel T1 and T2 distributions. The multidimensional results from this algorithm are difficult to visualize. We propose to apply this Bayesian approach in the brain to create T1 and T2 Gaussian distributions to represent various tissue types. Using these distributions as prior information, the Bayesian methodology is applied over the brain with a smaller dictionary. Results from this Bayesian approach with a smaller dictionary are weighted by the Gaussian probabilities and summed to create tissue probability maps in normal volunteers and a brain tumor patient.

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