Abstract

In MRI, magnetic susceptibility has been an important topic of research for a long time. A number of contrast mechanisms such as BOLD fMRI and SWI have their origins in the magnetic susceptibilty effects. In this presentation, I will explain the effects of magnetic susceptibility in MRI. Then a method to reconstruct magnetic susceptibility distribution from (local) field distribution map or phase image will be introduced. This approach of reconstructing susceptibilty distribution is referred to as quantitative susceptibility mapping (QSM) and has been a topic of research for the last 10+ years. Theoretically, generating a susceptibilty map is performed by dipole deconvolution, which is an ill-conditioned problem. A number of algorithms have been suggested to tackle this problem and many of them utilized a regularization factor to stablize the problem. Recently, deep learning methods such as QSMnet or DeepQSM have been proposed and generated high quality QSM maps, opening new directions for QSM reconstruction.
After covering the basics of QSM, I will discuss a few advaned topics in QSM, which includes generalization issues in deep learning QSM, additional sources of field perturbation, advanced techonology in QSM. Most of these topics are active areas of research that audience may find their interests. Lastly, a few popular applications of QSM such as high resolution deep gray matter imaging, hemoerrhage, Parkinson's disease, Alzheimer disease, multiple sclerosis, and so on.

Acknowledgements

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References

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