Overcoming the missing data challenge in clinical imaging using CycleGAN based on brain MRI in Multiple Sclerosis

Motivation: Clinical MRI datasets are not always comprehensive or consistent, limiting their use for secondary analysis. Goal(s): Investigating the suitability of a deep learning model named CycleGAN, with optional spectral normalization, for dealing with the missing sequence problems in clinical imaging as seen in multiple sclerosis (MS). Approach: Using standard brain MRI of 104 MS people, we implemented 2 CycleGAN models, one with and one without spectral normalization to compare. Results: CycleGAN performed competitively in image transformation between T1-weighted and T2-weighted images. Adding spectral normalization appears to improve performance, especially when the quality of training scans is inconsistent. Impact: CycleGAN-based model has the potential to generate non-acquired images not always needed in standard clinical imaging, as seen in brain MRI in MS, where the resulting images can help promote various secondary analysis studies including machine learning.