Enhancing MRI diagnosis of myocarditis using deep learning and generative adversarial networks
y
17 mar 2025
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Publicado en línea: 17 mar 2025
Recibido: 06 oct 2024
Aceptado: 03 feb 2025
DOI: https://doi.org/10.2478/amns-2025-0206
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© 2025 Haifeng Gui et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
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Gray scale of normal human tissue on T1 weighted and T2 weighted
| Name | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
|---|---|---|---|---|---|---|---|
| T1 weighted | White grey | Black | Gray | White | Black | White | Black |
| T2 weighted | Gray | Black | White grey | Gray | White | White grey | Black |
Comparison of MRI and CT performance
| Imaging characteristics | CT | MRI |
|---|---|---|
| Imaging signal | X-ray | Radio-frequency energy |
| Imaging magnetic field | No | The superposition of static magnetic field and gradient magnetic field |
| Adopted electromagnetic wave | A narrow beam of X-rays | Radio-frequency wave |
| Fault direction | Generally perpendicular to the body | Arbitrary direction |
| Data acquisition mode | Multidirectional projection | Multidirectional or unidirectional projection |
| Imaging time for each layer | Ultra-high speed CT can reach about 10ms | It varies by scan sequence |
| Image reconstruction mode | Back projection method, convolutional back projection method, iterative method, etc | Two dimensional Fourier transform imaging is the main method |
| Ionizing radiation | There’s X-ray radiation | Very safe with very little RF radiation |
| Real-time imaging | Realize | Realize |