A Z-Score Template Method for Person-Specific Augmentation of Clinical Brain MRI: An Investigation in Multiple...

Clinical MRI is essential for managing neurological disorders such as multiple sclerosis (MS) but often inconsistent, limiting secondary analyses for enhanced information characterization. Our goal was to establish a new Z-score template method for person-specific image augmentation, as compared to a common deep learning approach termed cycle generative adversarial network (CycleGAN), and test their utility using a treatment response predicting example in MS. We examined 148 MS participants (102 females) from two cohorts, 104 and 44, respectively, with equivalent sex ratio and age, and each included T1-weighted, T2-weighted, and fluid attenuated inversion recovery (FLAIR) brain MRI. The 104/148 participants were used for method development and 44/148 for held-out testing. Z-score templates were constructed using different sample sizes to compare. Z scores from the best template were used to create person- and sequence-specific images. Similar experiments were done using CycleGAN along with tests using the same cohort. Image quality was assessed using peak signal-to-noise ratio, structural similarity index, and root mean square error. Utility testing applied ResNet50-based deep learning models with cycling of the typically unavailable T1-weighted MRI. We found that Z-score template constructed with 75 individuals was the best. Using existing images, potentially unavailable MRI could be created using either method investigated. At an individual level, Z-score template synthesis was equivalent to CycleGAN results. Further, models trained with synthetic or source T1-weighted images achieved similar accuracies (0.82-0.84 ± 0.04 vs. 0.84 ± 0.02) in tests of treatment response prediction. Without using T1-weighted MRI, the model accuracy decreased to 0.72, and AUC decreased below chance. Overall, Z-score template appeared to be a competitive method for person-specific brain MRI augmentation, and synthesized images such as T1-weighted brain MRI have the potential to support downstream applications as seen in prediction of treatment response in MS.