1Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
2Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
3Department of Medical Imaging, Shanghai Medical School, Fudan University, Shanghai, China
4Xiamen Key Laboratory of Clinical Transformation of Imaging Big Data and Artificial Intelligence, Xiamen, Fujian, China
5Fujian Province Key Clinical Specialty for Medical Imaging, Xiamen, Fujian, China
6Department of Radiology, Xiang’ an Hospital of Xiamen University, Xiamen, Fujian, China
7Radiology Department, The First Affiliated Hospital of Xiamen University, Xiamen, China
8Shanghai Neusoft Medical Technology Co., Ltd, Shanghai, China
9School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China
10Human Phenome Institute, Shanghai, China
Presenting Author: Xiaobo Qu
Synopsis
Motivation:
Goals:
Approach:
Results:
Full abstract & presentation
The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.
Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.
To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.
1. O’Brien AT, Gil KE, Varghese J, Simonetti OP, Zareba KM. T2 mapping in myocardial disease: A comprehensive review. J Cardiovasc Magn Reson. 2022;24:33.
2. Yang Q, Wang Z, Guo K, Cai C, Qu X. Physics-driven synthetic data learning for biomedical magnetic resonance: The imaging physics-based data synthesis paradigm for artificial intelligence. IEEE Signal Process Mag. 2023;40:129–140.
3. Qiu S, Chen K, Li Y, et al. 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;92:2683–2695.
4. Raissi M, Perdikaris P, Karniadakis GE. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J Comput Phys. 2019;378:686–707.
5. Karniadakis GE, Kevrekidis IG, Lu L, Perdikaris P, Wang S, Yang L. Physics-informed machine learning. Nat Rev Phys. 2021;3:422–440.
6. Van Herten RLM, Chiribiri A, Breeuwer M, Veta M, Scannell CM. Physics-informed neural networks for myocardial perfusion MRI quantification. Med Image Anal. 2023;88:102940.
7. Zapf B, Haubner J, Kuchta M, Ringstad G, Eide PK, Mardal KA. Investigating molecular transport in the human brain from MRI with physics-informed neural networks. Sci Rep. 2022;12:15837.
8. Cai Q, Zhu L, Zhou J, Qian C, Tong R, Mei L, Jiang X, Xu Q, Qu X. T2 mapping with Bloch Equation-Informed Physical Intelligent Neural Network. Proc. In: Proceedings of the ISMRM & ISMRT Annual Meeting; 2025, Abstract 5038.
9. Han F, Liu Y, Wang J. Posteriori error neural network: A recovery-type posteriori error estimator based on neural network for diffusion problems. Comput Math Appl. 2023;143:169–188.
10. Mishra S, Molinaro R. Estimates on the generalization error of physics-informed neural networks for approximating PDEs. IMA J Numer Anal. 2023;43:1–43.
11. Mishra S, Molinaro R. Estimates on the generalization error of physics-informed neural networks for approximating a class of inverse problems for PDEs. IMA J Numer Anal. 2022;42:981–1022.
12. Hinshaw WS, Lent AH. An introduction to NMR imaging: From the Bloch equation to the imaging equation. Proc IEEE. 1983;71:338–350.
13. Dragomir S, Cerone P, Sofo A. Some remarks on the midpoint rule in numerical integration. RGMIA Res Rep Coll. 1998;1(2).
14. Lilliefors HW. On the Kolmogorov-Smirnov test for normality with mean and variance unknown. J Am Stat Assoc. 1967;62(318):399–402.
15. Habibzadeh F. Data distribution: Normal or abnormal? J Korean Med Sci. 2024;39:e35.