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Abstract #4254

Dictionary free anatomical segmentation of Magnetic Resonance Fingerprinting brain data with Independent Component Analysis

Rasim Boyacioglu1, Dan Ma1, Debra McGivney1, Louisa Onyewadume1, Ozden Kilinc1, Chaitra Badve1,2, Vikas Gulani1,2, and Mark Griswold1,2

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

Magnetic Resonance Fingerprinting signal evolutions are sensitized to certain tissue properties during data acquisition. The matching step can be suboptimal due to dictionary limitations or tissue related constraints (e.g. partial volume, magnetization exchange). Here, we propose to apply Independent Component Analysis (ICA) to 4D MRF data after image reconstruction without explicit dictionary matching for tissue characterization, lifting the requirement for a relaxation model. ICA of whole brain MRF data segments the brain into multiple components with single tissue types such as gray matter, white matter and CSF for healthy subjects and also tumor in the case of glioblastoma patients.

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