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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.05860 |
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| _version_ | 1866913575188037632 |
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| author | Dao, Duy-Phuong Yang, Hyung-Jeong Kim, Jahae |
| author_facet | Dao, Duy-Phuong Yang, Hyung-Jeong Kim, Jahae |
| contents | Alzheimers disease progresses slowly and involves complex interaction between various biological factors. Longitudinal medical imaging data can capture this progression over time. However, longitudinal data frequently encounter issues such as missing data due to patient dropouts, irregular follow-up intervals, and varying lengths of observation periods. To address these issues, we designed a diffusion-based model for 3D longitudinal medical imaging generation using single magnetic resonance imaging (MRI). This involves the injection of a conditioning MRI and time-visit encoding to the model, enabling control in change between source and target images. The experimental results indicate that the proposed method generates higher-quality images compared to other competing methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_05860 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Conditional Diffusion Model for Longitudinal Medical Image Generation Dao, Duy-Phuong Yang, Hyung-Jeong Kim, Jahae Computer Vision and Pattern Recognition Artificial Intelligence Alzheimers disease progresses slowly and involves complex interaction between various biological factors. Longitudinal medical imaging data can capture this progression over time. However, longitudinal data frequently encounter issues such as missing data due to patient dropouts, irregular follow-up intervals, and varying lengths of observation periods. To address these issues, we designed a diffusion-based model for 3D longitudinal medical imaging generation using single magnetic resonance imaging (MRI). This involves the injection of a conditioning MRI and time-visit encoding to the model, enabling control in change between source and target images. The experimental results indicate that the proposed method generates higher-quality images compared to other competing methods. |
| title | Conditional Diffusion Model for Longitudinal Medical Image Generation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2411.05860 |