Parameter optimization framework on wave gradients of Wave-CAIPI imaging

To propose a parameter optimization framework on wave gradients of Wave-CAIPI imaging for decreasing g-factor penalty and reducing reconstruction artifacts.

The influences of parameters on g-factor are theoretically analyzed. The average g-factor is chosen as a metric for parameter optimization, and then a fast calculation method is proposed to approximately and ultra-fast calculate the average g-factor. Based on this, a set of points in the function of the average g-factor with respect to the wave gradient parameters is calculated, and the optimal wave gradient parameters are found according to these points.

In vivo human brain experiments were performed on 3T MR scanners for the comparison experiments. The results show that the proposed parameter optimization framework is able to efficiently obtain optimal wave gradient parameters, which can achieve decreased g-factor penalty and less artifacts of reconstructions than the empirical parameters.

The proposed parameter optimization framework is computationally efficient and can optimize the wave gradient parameters of Wave-CAIPI imaging for better image quality than before.