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

S. So*, H.W. Park*, B. Kim, F.J. Fritz, B.A. Poser, A. Roebroeck, B. Bilgic

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Web of Science)


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.
Original languageEnglish
Pages (from-to)292-308
Number of pages17
JournalMagnetic Resonance in Medicine
Issue number1
Early online date28 Mar 2022
Publication statusPublished - Jul 2022


  • distortion correction
  • multicontrast MRI
  • quantitative MRI
  • stimulated echo
  • unsupervised parameter estimation
  • T2
  • T1

Cite this