BUDA‐MESMERISE: Rapid acquisition and unsupervised parameter estimation for T1, T2, M0, B0, and B1 maps. Issue 1 (28th March 2022)

• Record Type: Journal Article
• Authors: So, Seohee; Park, Hyun Wook; Kim, Byungjai; Fritz, Francisco J.; Poser, Benedikt A.; Roebroeck, Alard; Bilgic, Berkin
• Is Part Of: Magnetic resonance in medicine. Volume 88:Issue 1(2022)
• Page Start: 292
• Page End: 308
• Publication Date: 2022-03-28
• Subjects: distortion correction -- multicontrast MRI -- quantitative MRI -- stimulated echo -- unsupervised parameter estimation; Nuclear magnetic resonance -- Periodicals; Electron paramagnetic resonance -- Periodicals; 616.07548
• DOI: 10.1002/mrm.29228
• Languages: English
• ISSNs: 0740-3194

Abstract

Abstract : 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 T1, T2, and proton density (M0 ) parameter maps, along with B0 and B1 information from the acquired signals. Theory and Methods: 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 B0 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 T1, T2, relative proton density (M0), B0, and B1 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 T1, T2, M0, B0, and B1 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, T1, T2, M0, B0, and B1 maps at 1 × 1 × 5 mm 3 resolution in 50 s. The proposed unsupervised neural network provided noise‐robust parameter estimates from this rapid acquisition.