Ildar Khalidov1, Tian Liu1,
Xiaoyue Chen2, Moonsoo Jin2, Martin Prince1,
Yi Wang1
1Radiology, Weill Cornell Medical
College, NYC, NY, United States; 2Biomedical Engineering, Cornell
University, Ithaca, NY, United States
Quantitative
susceptibility mapping (QSM) has been developed as a technique that uses the
phase information from the MRI measurements to estimate susceptibility
changes in the imaged object. Moreover, it is possible to estimate the
magnetic moment of the region of interest, which gives way to quantitative
imaging of tracer particles in MRI. However, the inverse problem that needs
to be solved to recover the susceptibility map from the phase image is ill-posed:
1), the dipole kernel that links the two maps has a cone of zeros in Fourier
domain, and 2), regions of strong susceptibility change have low intensity
(and hence, unreliable phase data) due to T2* dephasing. In this work, we use
total variation-based regularization to tackle the inverse problem. Compared
to original weighted quadratic regularization in [1], the proposed TV
regularization significantly reduces the streaking artifacts from the areas
of susceptibility change. This is particularly important in animal imaging
where eventual air bubbles and/or voxel misclassifications at the
segmentation stage could lead to strong under-estimation of the quantities of
particles of interest.