Abstract #2366

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

Jingwen Yao¹, Melanie A. Morrison¹, Angela Jakary¹, Sivakami Avadiappan¹, Yicheng Chen^1,2,3, Julia Glueck⁴, Theresa Driscoll⁴, Michael Geschwind⁴, Alexandra Nelson⁴, Christopher P. Hess^1,4, and Janine M. Lupo^1,2

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

Synopsis

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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