Keywords: Parkinson's Disease, Quantitative Susceptibility mapping
Motivation: Predicting deep brain stimulation surgery outcome is motivated by shortcomings of the levodopa challenge test.
Goal(s): Preoperative quantitative susceptibility maps contain informative statistical information in the deep gray nuclei iron distributions. These distributions can be represented in the susceptibility radiomic features collected over the surgical targets such as the subthalamic nucleus.
Approach: A noise compensation model is introduced to predict surgical outcomes in the presence of both rater noise and dual raters.
Results: Use of this method improves the prediction based on presurgical imaging.
Impact: The introduced noise compensated radiomic model allows for predictions on datasets with more than one rater, reflecting a clinically relevant setting in which candidate selection can be improved.
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