Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
531-01-007 / 531-01-007 ISMRM Abstract

A Physics-Driven Neural Network with Parameter Embedding for Generating Quantitative MR Maps from Weighted Images

Primary: Acquisition & Reconstruction - Quantitative Imaging: head and neck
Secondary: Acquisition & Reconstruction - Signal Modeling
531-01-007 · Quantitative Imaging: Applications · Wednesday, 13 May, 8:20 AM–9:56 AM · Roof Terrace
531-01-007 · Quantitative Imaging: Applications · Wednesday, 13 May, 8:20 AM–9:56 AM · Roof Terrace
Accepted
Lingjing Chen 1, Yinqiao Yi1, Yang Song2, Xu Yan3, Shengfang Xu4, Dalin Zhu5, Mengqiu Cao6, Yan Zhou6, Chenglong Wang7, Guang Yang7
1Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
2MR Research Collaboration, Shanghai, China
3Siemens Healthcare China, Shanghai, China
4Gansu Provincial Maternity and Child-careHospital, China
5Gansu Provincial Maternity and Child-careHospital. Lanzhou, China
6.Shanghai Jiao Tong University School ofMedicine Affiliated Renii Hospital, China
7Shanghai Key Laboratory of Magnetic Resonance, Shanghai, China
Presenting Author: Lingjing Chen

Synopsis

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References

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3. Moya-Sáez E, Peña-Nogales Ó, Luis-García R, Alberola-López C. A deep learning approach for synthetic MRI based on two routine sequences and training with synthetic data. Comput Methods Programs Biomed. 2021;210:106371. doi:10.1016/j.cmpb.2021.106371 [doi]
4. Sun H, Wang L, Daskivich T, et al. Retrospective T2 quantification from conventional weighted MRI of the prostate based on deep learning. Front Radiol. 2023;3:1223377. Published 2023 Oct 11. doi:10.3389/fradi.2023.1223377 [doi]
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6. Qiu S, Wang L, Sati P, Christodoulou AG, Xie Y, Li D. Physics-guided self-supervised learning for retrospective T1 and T2 mapping from conventional weighted brain MRI: Technical developments and initial validation in glioblastoma. Magn Reson Med. 2024;92(6):2683-2695. doi:10.1002/mrm.30226 [doi]
7. Jacobs L, Mandija S, Liu H, van den Berg CAT, Sbrizzi A, Maspero M. Generalizable synthetic MRI with physics-informed convolutional networks. Med Phys. 2024;51(5):3348-3359. doi:10.1002/mp.16884 [doi]
8. Dar SU, Yurt M, Karacan L, Erdem A, Erdem E, Cukur T. Image Synthesis in Multi-Contrast MRI With Conditional Generative Adversarial Networks. IEEE Trans Med Imaging. 2019;38(10):2375-2388. doi:10.1109/TMI.2019.2901750 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/531-01-007