Feasibility study for reconstruction of knee MRI from one corresponding X-ray via CNN
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909540758323200 |
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| author | Wang, Zhe Chetouani, Aladine Jennane, Rachid |
| author_facet | Wang, Zhe Chetouani, Aladine Jennane, Rachid |
| contents | Generally, X-ray, as an inexpensive and popular medical imaging technique, is widely chosen by medical practitioners. With the development of medical technology, Magnetic Resonance Imaging (MRI), an advanced medical imaging technique, has already become a supplementary diagnostic option for the diagnosis of KOA. We propose in this paper a deep-learning-based approach for generating MRI from one corresponding X-ray. Our method uses the hidden variables of a Convolutional Auto-Encoder (CAE) model, trained for reconstructing X-ray image, as inputs of a generator model to provide 3D MRI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_13555 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Feasibility study for reconstruction of knee MRI from one corresponding X-ray via CNN Wang, Zhe Chetouani, Aladine Jennane, Rachid Image and Video Processing Computer Vision and Pattern Recognition Generally, X-ray, as an inexpensive and popular medical imaging technique, is widely chosen by medical practitioners. With the development of medical technology, Magnetic Resonance Imaging (MRI), an advanced medical imaging technique, has already become a supplementary diagnostic option for the diagnosis of KOA. We propose in this paper a deep-learning-based approach for generating MRI from one corresponding X-ray. Our method uses the hidden variables of a Convolutional Auto-Encoder (CAE) model, trained for reconstructing X-ray image, as inputs of a generator model to provide 3D MRI. |
| title | Feasibility study for reconstruction of knee MRI from one corresponding X-ray via CNN |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.13555 |