Abstract #2111

Robustness of Texture Features in Quantitative Susceptibility Mapping: Influence of Background Field Removal and Inversion Algorithms

Siyuan Fang¹, Ying Tang¹, Gaiying Li¹, Weiwei Zhao², Chunhui Yang³, Luguang Chen¹, Chao Ma⁴, Xi Wu³, and Jianqi Li¹

¹Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, China, ²Center for Magnetic Resonance Imaging Research & Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai, China, ³Department of Neurosurgery, Changhai Hospital, Shanghai, China, ⁴Department of Radiology, Changhai Hospital, Shanghai, China

Synopsis

Keywords: Quantitative Imaging, Quantitative Susceptibility mapping, Reproducibility, Inversion algorithms, Background Field Removal

Motivation: The smoothness of quantitative susceptibility mapping (QSM) images varies with reconstruction method used, which may impact the reproducibility of texture features.

Goal(s): To assess the robustness of texture features in QSM images across different background field removal and inversion algorithms used in QSM reconstruction

Approach: Three background field removal algorithms and two inversion algorithms were applied for QSM reconstruction. The inter-algorithm reproducibility of texture features was then assessed in a cohort of 40 subjects.

Results: The reproducibility of texture features varied across different combinations of background field removal and inversion algorithms in QSM reconstruction.

Impact: The robustness of texture features varies across different background field removal and inversion algorithms in QSM reconstruction. The impact of the QSM reconstruction algorithm on texture features should be carefully considered before clinical implementation.

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