  In MRI studies, the aggregation of imaging data from multiple acquisition
sites enhances sample size but may introduce site-related variabilities that
hinder consistency in subsequent analyses. Deep learning methods for image
translation have emerged as a solution for harmonizing MR images across sites.
In this study, we introduce IGUANe (Image Generation with Unified Adversarial
Networks), an original 3D model that leverages the strengths of domain
translation and straightforward application of style transfer methods for
multicenter brain MR image harmonization. IGUANe extends CycleGAN architecture
by integrating an arbitrary number of domains for training through a
many-to-one strategy. During inference, the model can be applied to any image,
even from an unknown acquisition site, making it a universal generator for
harmonization. Trained on a dataset comprising T1-weighted images from 11
different scanners, IGUANe was evaluated on data from unseen sites. The
assessments included the transformation of MR images with traveling subjects,
the preservation of pairwise distances between MR images within domains, the
evolution of volumetric patterns related to age and Alzheimer$^\prime$s disease
(AD), and the performance in age regression and patient classification tasks.
Comparisons with other harmonization and normalization methods suggest that
IGUANe better preserves individual information in MR images and is more
suitable for maintaining and reinforcing variabilities related to age and AD.
Future studies may further assess IGUANe in other multicenter contexts, either
using the same model or retraining it for applications to different image
modalities.
