  Purpose: To explore efficient encoding schemes for quantitative magnetization
transfer (qMT) imaging with few constraints on model
  Theory and Methods: We combine two recently proposed models in a
Bloch-McConnell equation: the dynamics of the free spin pool are confined to
the hybrid state, and the dynamics of the semi-solid spin pool are described by
the generalized Bloch model. We numerically optimize the flip angles and
durations of a train of radio frequency pulses to enhance the encoding of three
qMT parameters while accounting for all 8 parameters of the 2-pool model. We
sparsely sample each time frame along this spin dynamics with a 3D radial
koosh-ball trajectory, reconstruct the data with subspace modeling, and fit the
qMT model with a neural network for computational efficiency.
  Results: We extracted qMT parameter maps of the whole brain with an effective
resolution of 1.24mm from a 12.6-minute scan. In lesions of multiple sclerosis
subjects, we observe a decreased size of the semi-solid spin pool and longer
relaxation times, consistent with previous
  Conclusion: The encoding power of the hybrid state, combined with regularized
image reconstruction, and the accuracy of the generalized Bloch model provide
an excellent basis for efficient quantitative magnetization transfer imaging
with few constraints on model parameters.
