Keywords: Parkinson's Disease, Parkinson's Disease
Motivation: Applying radiological techniques for early diagnosis of Parkinson's disease (PD) is crucial for prognosis of patients. A simple, accurate technique with high patient compliance is still lacking.
Goal(s): To explore the feasibility of using visual neural networks based on T1-weighted images for detecting PD.
Approach: Patients with PD and healthy controls (HCs) underwent T1-weighted MRI scans. A convolutional neural network (CNN) architecture was developed. Interpretable maps were also drawn to investigate the efficiency of computerized neural networks.
Results: The CNN model achieved achieved good classification performance.Interpretable maps highlighted the critical regions for detecting PD.
Impact: CNN based on T1-weighted imaging is a reliable and accurate diagnostic tool for detecting PD. Interpretable maps improve the explainability of classification results in clinical applications.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords