Keywords: Analysis/Processing, Parkinson's Disease, Deep brain stimulation, fMRI, SPADE-VAE, image synthesis, latent space interpolation
Motivation: Optimizing deep brain stimulation (DBS) parameters for Parkinson’s disease (PD) has a high time to optimization per patient (TPP). Functional MRI (fMRI) and deep learning (DL)-based optimization can substantially reduce TPP, but faces data scarcity and class imbalance issues.
Goal(s): To improve DBS optimization by addressing DBS-fMRI data limitation and imbalance issues.
Approach: A SPADE-VAE model was trained to synthesize realistic optimal DBS-fMRI maps at varying optimization levels by blending latent representations of acquired optimal and non-optimal DBS-fMRI responses.
Results: The trained SPADE-VAE model generated realistic synthetic DBS-fMRI response maps, which improved the AE-MLP DBS parameter classification accuracy from 80% to 91%.
Impact: By producing realistic synthetic DBS-fMRI maps, the trained SPADE-VAE model addresses data gap issues in DL-based fMRI-DBS optimization, corrects class imbalances, enhances classification accuracy and ultimately reduces the time to optimization per patient in Parkinson’s disease treatment.
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