Keywords: Analysis/Processing, Machine Learning/Artificial Intelligence
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.
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