Abstract #3818

Decoding Sensitivity of Quantitative Susceptibility Mapping: Influence of Background Field Removal and Inversion Algorithms

Fahad Salman¹, Abhisri Ramesh¹, Mirjam Prayer¹, Ademola Adegbemigun^1,2, Thomas Jochmann^1,3, Niels Bergsland¹, Michael G. Dwyer^1,4, Robert Zivadinov^1,4, and Ferdinand Schweser^1,4

¹Buffalo Neuroimaging Analysis Center, Department of Neurology at the Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, United States, ²Jacobs Multiple Sclerosis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, United States, ³Department of Computer Science and Automation, Technische Universitat Ilmenau, Ilmenau, Germany, ⁴Center for Biomedical Imaging, Clinical and Translational Science Institute, University at Buffalo, The State University of New York, Buffalo, NY, United States

Synopsis

Keywords: Quantitative Imaging, Quantitative Susceptibility mapping, Dipole inversion, BFR, Background field, Sensitivity, Reproducibility, Reference region

Motivation: Quantitative Susceptibility Mapping (QSM) is widely applied in clinical research. However, its accuracy relies on the choice of background field removal (BFR) and inversion algorithms. This raises the question: What is the sensitivity of algorithms toward the detection of in-vivo group differences and over-time susceptibility changes?

Goal(s): Explore the impact of BFR and inversion algorithms on the detection of over-time susceptibility changes.

Approach: Utilizing six BFRs and twenty-one inversion algorithms, we studied the sensitivity to detect aging-related over-time susceptibility changes.

Results: RESHARP+iSWIM within overall DGM, RESHARP+AMP-PE in putamen, PDF+IterTIK in caudate, PDF+TKD in globus pallidus and RESHARP+iSWIM in thalamus demonstrated the highest sensitivity.

Impact: The importance of algorithm and reference region choice in QSM studies, impacting findings beyond demographics and clinical characteristics. Future research should employ varied QSM algorithms to assess their impact on longitudinal QSM changes, enhancing the quality of clinical investigations.

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