Deep Learning-Based Estimation of Myocardial Material Parameters from Cardiac MRI
-д хадгалсан:
| -д хэвлэсэн: | Bioengineering vol. 12, no. 4 (2025), p. 433 |
|---|---|
| Үндсэн зохиолч: | |
| Бусад зохиолчид: | , , |
| Хэвлэсэн: |
MDPI AG
|
| Нөхцлүүд: | |
| Онлайн хандалт: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Шошгууд: |
Шошго байхгүй, Энэхүү баримтыг шошголох эхний хүн болох!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3194492259 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2306-5354 | ||
| 024 | 7 | |a 10.3390/bioengineering12040433 |2 doi | |
| 035 | |a 3194492259 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a Chen, Yunhe |u Department of Aeronautics and Astronautics, Fudan University, Shanghai 200433, China; yunhechen23@m.fudan.edu.cn | |
| 245 | 1 | |a Deep Learning-Based Estimation of Myocardial Material Parameters from Cardiac MRI | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Background: Accurate estimation of myocardial material parameters is crucial to understand cardiac biomechanics and plays a key role in advancing computational modeling and clinical applications. Traditional inverse finite element (FE) methods rely on iterative optimization to infer these parameters, which is computationally expensive and time-consuming, limiting their clinical applicability. Methods: This study proposes a deep learning-based approach to rapidly and accurately estimate the left ventricular myocardial material parameters directly from routine cardiac magnetic resonance imaging (CMRI) data. A ResNet18-based model was trained on FEM-derived parameters from a dataset of 1288 healthy subjects. Results: The proposed model demonstrated high predictive accuracy on healthy subjects, achieving mean absolute errors of 0.0146 for <inline-formula>Ca</inline-formula> and 0.0139 for <inline-formula>Cb</inline-formula>, with mean relative errors below 5.00%. Additionally, we evaluated the model on a small pathological subset (including ARV and HCM cases). The results revealed that while the model maintained strong performance on healthy data, the prediction errors in the pathological samples were higher, indicating increased challenges in modeling diseased myocardial tissue. Conclusion: This study establishes a computationally efficient and accurate deep learning framework for estimating myocardial material parameters, eliminating the need for time-consuming iterative FE optimization. While the model shows promising performance on healthy subjects, further validation and refinement are required to address its limitations in pathological conditions, thereby paving the way for personalized cardiac modeling and improved clinical decision-making. | |
| 651 | 4 | |a United States--US | |
| 653 | |a Machine learning | ||
| 653 | |a Biomechanics | ||
| 653 | |a Magnetic resonance imaging | ||
| 653 | |a Datasets | ||
| 653 | |a Deep learning | ||
| 653 | |a Heart | ||
| 653 | |a Iterative methods | ||
| 653 | |a Optimization | ||
| 653 | |a Neural networks | ||
| 653 | |a Mathematical models | ||
| 653 | |a Errors | ||
| 653 | |a Stress analysis | ||
| 653 | |a Parameters | ||
| 653 | |a Decision making | ||
| 653 | |a Estimation | ||
| 653 | |a Business metrics | ||
| 653 | |a Parameter estimation | ||
| 700 | 1 | |a Zhang, Xiwen |u Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai 200032, China; 20307110438@fudan.edu.cn | |
| 700 | 1 | |a Huo Yongzhong |u Department of Aeronautics and Astronautics, Fudan University, Shanghai 200433, China; yunhechen23@m.fudan.edu.cn | |
| 700 | 1 | |a Wang, Shuo |u Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai 200032, China; 20307110438@fudan.edu.cn | |
| 773 | 0 | |t Bioengineering |g vol. 12, no. 4 (2025), p. 433 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3194492259/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3194492259/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3194492259/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |