2023 ISMRM and ISMRT Annual Meeting and Exhibition
3 June 2023 – 8 June 2023 · Toronto, Canada
0402 ISMRM Abstract

An Open-Source Self-navigated Multi-Echo Gradient Echo Acquisition for R2* and QSM mapping using Pulseq and Model-Based Reconstruction

Accepted
Xiaoqing Wang 1,2, Berkin Bilgic1,2, Daniel Gallichan3, Kwok-Shing Chan4, José P Marques4
1Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging, Cambridge, United States of America
2Department of Radiology, Harvard Medical School, Boston, United States of America
3Cardiff University Brain Research Imaging Centre, Cardiff, United Kingdom
4Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, Netherlands
Presenting Author: Xiaoqing Wang
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References

1. QMR Lucca 2022 Joint Workshop on MR phase, magnetic susceptibility and electrical properties mapping, Lucca, Italy.
2. Lancione M, Bosco P, Costagli M, et al. Multi-centre and multi-vendor reproducibility of a standardized protocol for quantitative susceptibility Mapping of the human brain at 3T. Physica Medica 2022;103:37–45 doi: 10.1016/j.ejmp.2022.09.012 . [doi]
3. Ropele S, Wattjes MP, Langkammer C, et al. Multicenter R2* mapping in the healthy brain. Magn. Reson. Med. 2014;71:1103–1107.
4. Leutritz T, Seif M, Helms G, et al. Multiparameter mapping of relaxation (R1, R2*), proton density and magnetization transfer saturation at 3 T: A multicenter dual-vendor reproducibility and repeatability study. Hum. Brain Mapp. 2020;41:4232–4247.
5. Rua C, Clarke WT, Driver ID, et al. Multi-centre, multi-vendor reproducibility of 7T QSM and R2* in the human brain: Results from the UK7T study. Neuroimage 2020;223:117358.
6. Layton KJ, Kroboth S, Jia F, et al. Pulseq: A rapid and hardware-independent pulse sequence prototyping framework. Magn. Reson. Med. 2017;77:1544–1552.
7. Wen J, Cross AH, Yablonskiy DA. On the role of physiological fluctuations in quantitative gradient echo MRI: implications for GEPCI, QSM, and SWI. Magn. Reson. Med. 2015;73:195–203.
8. Versluis MJ, Sutton BP, de Bruin PW, Börnert P, Webb AG, van Osch MJ. Retrospective image correction in the presence of nonlinear temporal magnetic field changes using multichannel navigator echoes. Magn. Reson. Med. 2012;68:1836–1845.
9. Tan Z, Unterberg-Buchwald C, Blumenthal M, et al. Free-Breathing Liver Fat, R_2^* and B_0 Field Mapping Using Multi-Echo Radial FLASH and Regularized Model-based Reconstruction. 2021 doi: 10.48550/arXiv.2101.02788 . [doi]
10. Uecker M, Tamir JI, Ong F, Lustig M. The BART toolbox for computational Magnetic Resonance Imaging. http://wwwuser.gwdg.de/~muecker1/basp-uecker2.pdf. Accessed November 8, 2022.
11. Chan K-S, Marques JP. SEPIA—Susceptibility mapping pipeline tool for phase images. Neuroimage 2021;227:117611.
12. Lai K-W, Aggarwal M, van Zijl P, Li X, Sulam J. Learned Proximal Networks for Quantitative Susceptibility Mapping. Med. Image Comput. Comput. Assist. Interv. 2020;12262:125–135.
13. Veraart J, Novikov DS, Christiaens D, Ades-Aron B, Sijbers J, Fieremans E. Denoising of diffusion MRI using random matrix theory. Neuroimage 2016;142:394–406.
14. Bilgic B, Gagoski BA, Cauley SF, et al. Wave-CAIPI for highly accelerated 3D imaging. Magn. Reson. Med. 2015;73:2152–2162.

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