Tian Liu1, Cynthia Wisnieff2, 3, Min Lou4, Weiwei Chen5, Pascal Spincemaille3, Yi Wang2, 3
1MedImageMetric LLC, New York, NY, United States; 2Biomedical Engineering, Cornell University, Ithaca, NY, United States; 3Radiology, Weill Cornell Medical College, New York, NY, United States; 4Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; 5Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science& Technology (HUST), Wuhan, Hubei, China
Quantitative Susceptibility Mapping is becoming an increasingly active area of scientific and clinical investigations. In practical applications, there are sources of errors for QSM including noise, phase unwrapping failures and signal model inaccuracy. To improve the robustness of QSM quality, we propose a nonlinear data fitting for field map estimation and dipole inversion to reduce noise and phase unwrapping failures, and a method for model error reduction through iterative tuning. Compared to the previous linear QSM method, this nonlinear QSM method reduced checkerboard pattern in high susceptibility regions in healthy subjects and markedly reduced artifacts in patients with intracerebral hemorrhages.