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: | Hajar Emami |
| Institution: | Wayne state university |
| Department: | Computer Science |
| Country: | |
| Proposed Analysis: | Multimodal medical imaging is crucial in various clinical scenarios like early disease diagnosis and treatment. Different modalities of medical images provide complementary information in order to make accurate clinical decision. However, acquisition of multiple modalities is time-consuming, costly and includes radiation exposure side effects to patients. Therefore, the motivation has been grown for an automated imaging modality transfer approach that estimates missing imaging modalities from the available ones. Positron emission tomography (PET) imaging is widely used for diagnosing a number of neurological diseases such as Dementia, Epilepsy, Head and Neck Cancer. However, high-quality PET acquisition procedure involves radiotracer injection and a high cost associated with specialized tools, and expertise. Based on these motivations, we want to develop a novel deep learning model for MRI-to-PET image generation to reduce time and cost of imaging acquisition, and also reduce radiation exposure side effects. |
| Additional Investigators |
