Keywords: Parkinson's Disease, Analysis/Processing, Nigrosome, QSM, ML
Motivation: Parkinson’s disease diagnosis, in early stages, still strongly relies on qualitative clinical evaluation, rather than quantitative data, often resulting in misdiagnosis.
Goal(s): The goal was to identify PD patients when Nigrosome-1 is still visible on MRI imaging
Approach: We extracted quantitative structural data of Nigrosome-1 and trained a Machine Learning model to perform a classification task
Results: Quantitative features, Volume of Nigrosome-1 in particular, proved to be a good feature to differentiate PD from HC, performing 0.87 accuracy, and 0.94 AUC-ROC
Impact: These results support the need to integrate visual assessment of N1 with a quantitative assessment of its structure and susceptibility properties to better characterize PD pathology
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