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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