BUDA-MESMERISE: Rapid acquisition and unsupervised parameter estimation for T1, T2, M0, B0, and B1 maps

Purpose Rapid acquisition scheme and parameter estimation method are proposed to acquire distortion-free spin- and stimulated-echo signals and combine the signals with a physics-driven unsupervised network to estimate T-1, T-2, and proton density (M-0) parameter maps, along with B-0 and B-1 information from the acquired signals. Theory and Methods An imaging sequence with three 90 degrees RF pulses is utilized to acquire spin- and stimulated-echo signals. We utilize blip-up/-down acquisition to eliminate geometric distortion incurred by the effects of B-0 inhomogeneity on rapid EPI acquisitions. For multislice imaging, echo-shifting is applied to utilize dead time between the second and third RF pulses to encode information from additional slice positions. To estimate parameter maps from the spin- and stimulated-echo signals with high fidelity, 2 estimation methods, analytic fitting and a novel unsupervised deep neural network method, are developed. Results The proposed acquisition provided distortion-free T-1, T-2, relative proton density (M0), B-0, and B-1 maps with high fidelity both in phantom and in vivo brain experiments. From the rapidly acquired spin- and stimulated-echo signals, analytic fitting and the network-based method were able to estimate T-1, T-2, M-0, B-0, and B-1 maps with high accuracy. Network estimates demonstrated noise robustness owing to the fact that the convolutional layers take information into account from spatially adjacent voxels. Conclusion The proposed acquisition/reconstruction technique enabled whole-brain acquisition of coregistered, distortion-free, T-1, T-2, M-0, B-0, and B-1 maps at 1 x 1 x 5 mm(3) resolution in 50 s. The proposed unsupervised neural network provided noise-robust parameter estimates from this rapid acquisition.

Automated summary

This article proposes a rapid acquisition and parameter estimation method for distortion-free spin- and stimulated-echo signals, utilizing an unsupervised deep neural network to estimate T1, T2, M0, B0, and B1 parameter maps. The results demonstrate high fidelity estimation of these parameter maps using both analytic fitting and the network-based method.