BUDA-MESMERISE: Rapid acquisition and unsupervised parameter estimation for T 1 , T 2 , M 0 , B 0 , and B 1 maps

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 , T , and proton density (M ) parameter maps, along with B and B information from the acquired signals. An imaging sequence with three 90° 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 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. The proposed acquisition provided distortion-free T , T , relative proton density (M0), B , and B 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 , T , M , B , and B 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. The proposed acquisition/reconstruction technique enabled whole-brain acquisition of coregistered, distortion-free, T , T , M , B , and B maps at 1 × 1 × 5 mm resolution in 50 s. The proposed unsupervised neural network provided noise-robust parameter estimates from this rapid acquisition.