Abstract #4476

Improving Susceptibility Mapping of Veins using a K-Space Iterative Approach

Jin Tang¹, Saifeng Liu¹, Jaladhar Neelavalli², E. Mark Haacke^2,3

¹School of Biomedical Engineering, McMaster University, Hamilton, Ontario, Canada; ²The MRI Institute for Biomedical Research, Detroit, MI, United States; ³Academic Radiology, Wayne State University, Detroit, MI, United States

Mapping susceptibility from field perturbation data is a difficult inverse problem. Here we present a unique k-space iteration/image processing approach which dramatically reduced reconstruction streak artefacts caused by an ill-posed problem of inverse filter and simultaneously improved the accuracy of susceptibility quantification. This method could potentially be used for quantitative in vivo venous oxygen saturation measurement using SWI data.