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Abstract #2366

Comparison of Quantitative Susceptibility Mapping Methods for Brain Iron Imaging at 7T

Jingwen Yao1, Melanie A. Morrison1, Angela Jakary1, Sivakami Avadiappan1, Yicheng Chen1,2,3, Julia Glueck4, Theresa Driscoll4, Michael Geschwind4, Alexandra Nelson4, Christopher P. Hess1,4, and Janine M. Lupo1,2
1Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States, 2Graduate Program in Bioengineering, UCSF/UC Berkeley, San Francisco, CA, United States, 3Facebook Inc., Mountain View, CA, United States, 4Department of Neurology, University of California, San Francisco, San Francisco, CA, United States


Quantitative susceptibility mapping (QSM) is a promising tool to investigate iron dysregulation in neurodegenerative diseases. A diverse range of methods has been proposed to generate accurate and robust QSM images. In this study, we evaluated the performance of different dipole inversion algorithms for brain iron imaging at 7T, including iLSQR, iterative methods with regularization (STAR-QSM, FANSI, HD-QSM, MEDI), single-step methods (QSIP, SSTV, SSTGV), and deep learning methods (QSMGAN, QSMnet+). We found that SSTV/SSTGV provided the best performance in terms of correlation with age, correlation with iron, and the differentiation between healthy control and premanifest Huntington’s disease individuals.

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