Synthesizing beta-amyloid PET images from T1-weighted Structural MRI: A Preliminary Study
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866914961143365632 |
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| author | Lyu, Qing Kim, Jin Young Kim, Jeongchul Whitlow, Christopher T |
| author_facet | Lyu, Qing Kim, Jin Young Kim, Jeongchul Whitlow, Christopher T |
| contents | Beta-amyloid positron emission tomography (A$β$-PET) imaging has become a critical tool in Alzheimer's disease (AD) research and diagnosis, providing insights into the pathological accumulation of amyloid plaques, one of the hallmarks of AD. However, the high cost, limited availability, and exposure to radioactivity restrict the widespread use of A$β$-PET imaging, leading to a scarcity of comprehensive datasets. Previous studies have suggested that structural magnetic resonance imaging (MRI), which is more readily available, may serve as a viable alternative for synthesizing A$β$-PET images. In this study, we propose an approach to utilize 3D diffusion models to synthesize A$β$-PET images from T1-weighted MRI scans, aiming to overcome the limitations associated with direct PET imaging. Our method generates high-quality A$β$-PET images for cognitive normal cases, although it is less effective for mild cognitive impairment (MCI) patients due to the variability in A$β$ deposition patterns among subjects. Our preliminary results suggest that incorporating additional data, such as a larger sample of MCI cases and multi-modality information including clinical and demographic details, cognitive and functional assessments, and longitudinal data, may be necessary to improve A$β$-PET image synthesis for MCI patients. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_18282 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Synthesizing beta-amyloid PET images from T1-weighted Structural MRI: A Preliminary Study Lyu, Qing Kim, Jin Young Kim, Jeongchul Whitlow, Christopher T Image and Video Processing Computer Vision and Pattern Recognition Medical Physics Beta-amyloid positron emission tomography (A$β$-PET) imaging has become a critical tool in Alzheimer's disease (AD) research and diagnosis, providing insights into the pathological accumulation of amyloid plaques, one of the hallmarks of AD. However, the high cost, limited availability, and exposure to radioactivity restrict the widespread use of A$β$-PET imaging, leading to a scarcity of comprehensive datasets. Previous studies have suggested that structural magnetic resonance imaging (MRI), which is more readily available, may serve as a viable alternative for synthesizing A$β$-PET images. In this study, we propose an approach to utilize 3D diffusion models to synthesize A$β$-PET images from T1-weighted MRI scans, aiming to overcome the limitations associated with direct PET imaging. Our method generates high-quality A$β$-PET images for cognitive normal cases, although it is less effective for mild cognitive impairment (MCI) patients due to the variability in A$β$ deposition patterns among subjects. Our preliminary results suggest that incorporating additional data, such as a larger sample of MCI cases and multi-modality information including clinical and demographic details, cognitive and functional assessments, and longitudinal data, may be necessary to improve A$β$-PET image synthesis for MCI patients. |
| title | Synthesizing beta-amyloid PET images from T1-weighted Structural MRI: A Preliminary Study |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Medical Physics |
| url | https://arxiv.org/abs/2409.18282 |