Simultaneous 3D quantitative magnetization transfer imaging and susceptibility mapping
Name
Magnetic Resonance in Med - 2025 - Jang - Simultaneous 3D quantitative magnetization transfer imaging and susceptibility.pdf
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2.94 MB
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Author(s)
Jang, Albert
Chan, Kwok‐Shing
Mareyam, Azma
Stockmann, Jason
Huang, Susie Yi
Wang, Nian
Jang, Hyungseok
Lee, Hong‐Hsi
Liu, Fang
Date Issued
March 17, 2025
Journal
Magnetic Resonance in Medicine
Publisher
Wiley
Citation
Jang A, Chan K-S, Mareyam A, et al. Simultaneous 3D quantitative magnetization transfer imaging and susceptibility mapping. Magn Reson Med. 2025; 94: 735-744.
Version
Final published version
Abstract
Purpose: Introduce a unified acquisition and modeling strategy to simul-taneously quantify magnetization transfer (MT), tissue susceptibility (𝜒)and T∗2 .
Theory and Methods: Magnetization transfer is induced through the appli-cation of off-resonance irradiation between excitation and acquisition of anRF-spoiled gradient-echo scheme, where free pool spin–lattice relaxation (TF1 ),macromolecular proton fraction (f ) and magnetization exchange rate (kF ) werecalculated by modeling the magnitude of the MR signal using a binary spin-bathMT model with B+1 inhomogeneity correction via Bloch-Siegert shift. Simultane-ously, a multi-echo acquisition is incorporated into this framework to measurethe time evolution of both signal magnitude and phase, which was further mod-eled for estimating T∗2 and tissue susceptibility. In this work, we demonstratethe feasibility of this new acquisition and modeling strategy in vivo on the braintissue.
Results: In vivo brain experiments were conducted on five healthy subjects tovalidate our method. Utilizing an analytically derived signal model, we simul-taneously obtained 3D TF1 , f , kF , 𝜒 and T∗2 maps of the whole brain. Our resultsfrom the brain regional analysis show good agreement with those previouslyreported in the literature, which used separate MT and QSM methods.Conclusion: A unified acquisition and modeling strategy based on an analyticalsignal model that fully leverages both the magnitude and phase of the acquiredsignals was demonstrated and validated for simultaneous MT, susceptibility andT∗2 quantification that are free from B+1 bias.
MIT Department
Martinos Imaging Center (McGovern Institute for Brain Research at MIT)
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DOI of Published Version
https://doi.org/10.1002/mrm.30493
