1617 ISMRM Abstract

Joint MR T1 and T2* Parameter Mapping with Scan Specific Unsupervised Networks

1617 · Relaxometry & Diffusion I · Monday, 5 June, 8:15 AM–9:15 AM · Exhibition Halls D/E

Keywords: Image Reconstruction Quantitative Imaging

Accepted

Amir Heydari ¹, Tae Hyung Kim^2,3,4, Abbas Ahmadi¹, Berkin Bilgic^3,4

¹Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran (Islamic Republic of)

²Department of Computer Engineering, Hongik University, Seoul, Korea, Republic of

³Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, United States of America

⁴Radiology, Harvard Medical School, Boston, United States of America

Presenting Author: Amir Heydari

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References

1. [20] B. Yaman, S. A. H. Hosseini, and M. Akçakaya, “Zero-shot self-supervised learning for MRI reconstruction,” arXiv Prepr. arXiv2102.07737, 2021. [21] Pruessmann, K. P., Weiger, M., Scheidegger, M. B. & Boesiger, P. SENSE: sensitivity encoding for fast MRI. Magn. Reson. Med. An Off. J. Int. Soc. Magn. Reson. Med. 42, 952–962 (1999). [22] Chen, C., Li, Y. & Huang, J. Calibrationless parallel MRI with joint total variation regularization. in International Conference on Medical Image Computing and Computer-Assisted Intervention 106–114 (Springer, 2013). [23] Kim, T. H., Garg, P. & Haldar, J. P. LORAKI: Autocalibrated recurrent neural networks for autoregressive MRI reconstruction in k-space. arXiv Prepr. arXiv1904.09390 (2019).

Cite this abstract

http://echo.ismrm.org/p/ISMRM2023/1617