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Abstract #3237

Label-informed Data Augmentation for DBS Optimization: Synthesizing fMRI Maps with SPADE-VAE Network to Improve DBS Parameter Classification

Jianwei Qiu1, Afis Ajala1, Jürgen Germann2, Brendan Santyr2, Desmond Yeo1, Luca Marinelli1, Alexandre Boutet2, and Andres Lozano2
1Technology and Innovation Center, GE HealthCare, Niskayuna, NY, United States, 2University Health Network and University of Toronto, Toronto, ON, Canada

Synopsis

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.

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Keywords