2022 Joint Annual Meeting ISMRM-ESMRMB and ISMRT 31st Annual Meeting
7 May 2022 – 15 May 2022 · London, United Kingdom
0435 ISMRM Abstract

Rapid Quantitative Imaging Using Wave-Encoded Model-Based Deep Learning for Joint Reconstruction

Accepted
Jaejin Cho 1,2, Borjan Gagoski2,3, Tae Hyung Kim1,2, Qiyuan Tian1,2, Robert Frost1,2, Itthi Chatnuntawech4, Berkin Bilgic1,2,5
1Athinoula A. Martinos Center for Biomedical Imaging, Cambridge, United States of America
2Department of Radiology, Harvard Medical School, Boston, United States of America
3Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children's Hospital, Boston, United States of America
4National Nanotechnology Center, Pathum Thani, Thailand
5Harvard-MIT Division of Health Sciences and Technology, Cambridge, United States of America
Presenting Author: Jaejin Cho
Full abstract & presentation

The full text, figures, and any recorded presentation for this abstract are not shown here.

This content is freely available and no longer restricted to registered attendees. Log in or create a free account to view it.

Log in

References

1. Bilgic B, Gagoski BA, Cauley SF, et al. Wave-CAIPI for highly accelerated 3D imaging. Magn. Reson. Med. 2015;73:2152–2162 doi: 10.1002/mrm.25347. [doi]
2. Gagoski BA, Bilgic B, Eichner C, et al. RARE/turbo spin echo imaging with simultaneous multislice Wave-CAIPI: RARE/TSE with SMS Wave-CAIPI. Magn. Reson. Med. 2015;73:929–938 doi: 10.1002/mrm.25615. [doi]
3. Cho J, Tian Q, Frost R, Chatnuntawech I, Bilgic B. Wave-Encoded Model-Based Deep Learning with Joint Reconstruction and Segmentation. In: Proceedings of the 29th Scientific Meeting of ISMRM. Online Conference; 2021. p.
4. Aggarwal HK, Mani MP, Jacob M. MoDL: Model Based Deep Learning Architecture for Inverse Problems. 2017 doi: 10.1109/TMI.2018.2865356. [doi]
5. Aggarwal HK, Mani MP, Jacob M. MoDL-MUSSELS: Model-Based Deep Learning for Multi-Shot Sensitivity Encoded Diffusion MRI. IEEE Trans. Med. Imaging 2020;39:1268–1277 doi: 10.1109/TMI.2019.2946501. [doi]
6. Kvernby S, Warntjes MJB, Haraldsson H, Carlhäll C-J, Engvall J, Ebbers T. Simultaneous three-dimensional myocardial T1 and T2 mapping in one breath hold with 3D-QALAS. J. Cardiovasc. Magn. Reson. 2014;16:102 doi: 10.1186/s12968-014-0102-0. [doi]
7. Kvernby S, Warntjes M, Engvall J, Carlhäll C-J, Ebbers T. Clinical feasibility of 3D-QALAS – Single breath-hold 3D myocardial T1- and T2-mapping. Magn. Reson. Imaging 2017;38:13–20 doi: 10.1016/j.mri.2016.12.014. [doi]
8. Fujita S, Hagiwara A, Hori M, et al. Three-dimensional high-resolution simultaneous quantitative mapping of the whole brain with 3D-QALAS: An accuracy and repeatability study. Magn. Reson. Imaging 2019;63:235–243 doi: 10.1016/j.mri.2019.08.031. [doi]
9. Fujita S, Hagiwara A, Takei N, et al. Accelerated Isotropic Multiparametric Imaging by High Spatial Resolution 3D-QALAS With Compressed Sensing: A Phantom, Volunteer, and Patient Study. Invest. Radiol. 2021;56:292–300 doi: 10.1097/RLI.0000000000000744. [doi]
10. Breuer FA, Blaimer M, Mueller MF, et al. Controlled aliasing in volumetric parallel imaging (2D CAIPIRINHA). Magn. Reson. Med. 2006;55:549–556 doi: 10.1002/mrm.20787. [doi]
11. Dale AM, Fischl B, Sereno MI. Cortical Surface-Based Analysis: I. Segmentation and Surface Reconstruction. NeuroImage 1999;9:179–194 doi: 10.1006/nimg.1998.0395. [doi]
12. Desikan RS, Ségonne F, Fischl B, et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 2006;31:968–980.
13. Eo T, Jun Y, Kim T, Jang J, Lee H-J, Hwang D. KIKI-net: cross-domain convolutional neural networks for reconstructing undersampled magnetic resonance images. Magn. Reson. Med. 2018;80:2188–2201 doi: 10.1002/mrm.27201. [doi]
14. Han Y, Sunwoo L, Ye JC. $k$ -Space Deep Learning for Accelerated MRI. IEEE Trans. Med. Imaging 2020;39:377–386 doi: 10.1109/TMI.2019.2927101. [doi]
15. Griswold MA, Jakob PM, Heidemann RM, et al. Generalized autocalibrating partially parallel acquisitions (GRAPPA). Magn. Reson. Med. 2002;47:1202–1210 doi: 10.1002/mrm.10171. [doi]

Cite this abstract