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

Seohee So, Hyun Wook Park, Byungjai Kim, Francisco J Fritz, Benedikt A Poser, Alard Roebroeck, Berkin Bilgic

Journal: Magnetic resonance in medicine 2022;88(1):292-308

PMID: 35344611

Abstract

[{"label":"PURPOSE","text":"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."},{"label":"THEORY AND METHODS","text":"An imaging sequence with three 90\u00b0 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."},{"label":"RESULTS","text":"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."},{"label":"CONCLUSION","text":"The proposed acquisition\/reconstruction technique enabled whole-brain acquisition of coregistered, distortion-free, T , T , M , B , and B maps at 1\u2009\u00d7\u20091\u2009\u00d7\u20095\u2009mm resolution in 50\u2009s. The proposed unsupervised neural network provided noise-robust parameter estimates from this rapid acquisition."},{"copyright":"\u00a9 2022 International Society for Magnetic Resonance in Medicine."}]
Address: School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.; Institute of Systems Neuroscience, Center for Experimental Medicine, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany.; Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands.; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.; Department of Radiology, Harvard Medical School, Charlestown, Massachusetts, USA.; Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.

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