There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | 彬 孙 |
| Institution: | 中国科学院大学 |
| Department: | 深圳先进技术研究院 |
| Country: | |
| Proposed Analysis: | Conditional Generative Adversarial Networks (cGANs) are a set of methods able to synthesize images that match a given condition. However, existing models designed for natural images are impractical to generate high-quality 3D medical images due to enormous computa- tion. To address this issue, most cGAN models used in the medical field process either 2D slices or small 3D crops and join them together in subsequent steps to reconstruct the full-size 3D image. However, these approaches often cause spatial inconsistencies in adjacent slices or crops, and the changes specified by the target condition may not consider the 3D image as a whole. To address these problems, we propose a novel cGAN that can synthesize high-quality 3D MR images at different stages of the Alzheimer’s disease (AD). |
| Additional Investigators |
