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| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.14211 |
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| _version_ | 1866912768030932992 |
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| author | Zhang, Mengxue Cai, Qingrui Chen, Yinyin Jin, Hang Zhou, Jianjun Guo, Qiu Zhao, Peijun Mao, Zhiping Zhang, Xingxing Xia, Yuyu Jiang, Xianwang Xu, Qin Xiong, Chunyan Zhou, Yirong Wang, Chengyan Qu, Xiaobo |
| author_facet | Zhang, Mengxue Cai, Qingrui Chen, Yinyin Jin, Hang Zhou, Jianjun Guo, Qiu Zhao, Peijun Mao, Zhiping Zhang, Xingxing Xia, Yuyu Jiang, Xianwang Xu, Qin Xiong, Chunyan Zhou, Yirong Wang, Chengyan Qu, Xiaobo |
| contents | Physics-Informed Neural Networks (PINN) are emerging as a promising approach for quantitative parameter estimation of Magnetic Resonance Imaging (MRI). While existing deep learning methods can provide an accurate quantitative estimation of the T2 parameter, they still require large amounts of training data and lack theoretical support and a recognized gold standard. Thus, given the absence of PINN-based approaches for T2 estimation, we propose embedding the fundamental physics of MRI, the Bloch equation, in the loss of PINN, which is solely based on target scan data and does not require a pre-defined training database. Furthermore, by deriving rigorous upper bounds for both the T2 estimation error and the generalization error of the Bloch equation solution, we establish a theoretical foundation for evaluating the PINN's quantitative accuracy. Even without access to the ground truth or a gold standard, this theory enables us to estimate the error with respect to the real quantitative parameter T2. The accuracy of T2 mapping and the validity of the theoretical analysis are demonstrated on a numerical cardiac model and a water phantom, where our method exhibits excellent quantitative precision in the myocardial T2 range. Clinical applicability is confirmed in 94 acute myocardial infarction (AMI) patients, achieving low-error quantitative T2 estimation under the theoretical error bound, highlighting the robustness and potential of PINN. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_14211 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Error Bound Analysis of Physics-Informed Neural Networks-Driven T2 Quantification in Cardiac Magnetic Resonance Imaging Zhang, Mengxue Cai, Qingrui Chen, Yinyin Jin, Hang Zhou, Jianjun Guo, Qiu Zhao, Peijun Mao, Zhiping Zhang, Xingxing Xia, Yuyu Jiang, Xianwang Xu, Qin Xiong, Chunyan Zhou, Yirong Wang, Chengyan Qu, Xiaobo Biological Physics Artificial Intelligence Physics-Informed Neural Networks (PINN) are emerging as a promising approach for quantitative parameter estimation of Magnetic Resonance Imaging (MRI). While existing deep learning methods can provide an accurate quantitative estimation of the T2 parameter, they still require large amounts of training data and lack theoretical support and a recognized gold standard. Thus, given the absence of PINN-based approaches for T2 estimation, we propose embedding the fundamental physics of MRI, the Bloch equation, in the loss of PINN, which is solely based on target scan data and does not require a pre-defined training database. Furthermore, by deriving rigorous upper bounds for both the T2 estimation error and the generalization error of the Bloch equation solution, we establish a theoretical foundation for evaluating the PINN's quantitative accuracy. Even without access to the ground truth or a gold standard, this theory enables us to estimate the error with respect to the real quantitative parameter T2. The accuracy of T2 mapping and the validity of the theoretical analysis are demonstrated on a numerical cardiac model and a water phantom, where our method exhibits excellent quantitative precision in the myocardial T2 range. Clinical applicability is confirmed in 94 acute myocardial infarction (AMI) patients, achieving low-error quantitative T2 estimation under the theoretical error bound, highlighting the robustness and potential of PINN. |
| title | Error Bound Analysis of Physics-Informed Neural Networks-Driven T2 Quantification in Cardiac Magnetic Resonance Imaging |
| topic | Biological Physics Artificial Intelligence |
| url | https://arxiv.org/abs/2512.14211 |