Keywords: Image Reconstruction, Brain, model-based recontructionPhase-based T2 mapping acquires two GRE volumes with opposite RF phase increments, then computes the phase difference and relates this to T2 values through Bloch modeling. We exploit the redundancy in these two acquisitions by posing this as a nonlinear inverse problem, and directly estimate the phase difference from k-space. This reduces the problem to the estimation of a complex M0 volume, and a real-valued phase difference, thus reducing the number of real-valued unknowns from 4 to 3 per voxel. The proposed reconstruction flexibly admits additional regularization, and yields higher quality images from undersampled acquisitions for rapid T2 mapping.
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