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Abstract #3308

Feature and voxel fidelity constraints improve the accuracy of direct inversion quantitative susceptibility mapping in deep gray matter

Jorge Campos Pazmiño1, Véronique Fortier1,2, and Ives Levesque1,2,3
1Medical Physics Unit, McGill University, Montreal, QC, Canada, 2Medical Imaging, McGill University Health Centre, Montreal, QC, Canada, 3Research Institute of the McGill University Health Centre, Montreal, QC, Canada


We have designed a novel QSM algorithm that addresses some of the limitations of existing techniques that combine the background removal and dipole inversion steps in a single step. We propose that the solution to the direct inversion problem can be aided by an iterative k-space algorithm and the inclusion of a priori information that represents feature-based and voxel-fidelity-based constraints. The considered approach, when compared with other techniques, resulted in a more accurate depiction of the susceptibility in high susceptibility deep gray matter (dGM) structures without sacrificing performance in regions like the cortex of the brain.

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