Towards rapid T2 mapping using geometry in bSSFP imaging

Motivation: Quantitative T2 mapping is useful for indication of neuropathological states such as multiple sclerosis, but current methods are time consuming and can be challenging at low T2 values needed to indicate pathology. Goal(s): To compute T2 maps efficiently and analytically. Approach: T2 maps are computed by exploiting the geometry of the bSSFP signal. The algorithm is enhanced with added regularization, linearization, and solution combination, and is evaluated in simulations and phantom images. Results: The improved algorithm demonstrates precision in simulations and a phantom, especially for lower T2 values. Reconstructed phantom T2 values were realistic, indicating its promise as a diagnostic tool. Impact: Standard quantitative T2 mapping requires significant scan time for adequate fitting; here an analytical T2 mapping method is demonstrated that doesn’t require additional scan time beyond the associated artifact-free bSSFP images generated, inspiring further exploration.