In MRI multicentric studies, inter-scanner harmonization is necessary to avoid taking into account variations due to technical differences in the analysis. In this study, we focused on CycleGAN models for 3D T1 weighted brain images harmonization. More precisely, we didn't follow the classical 2D CycleGAN architecure and developped a 3D cycleGAN model. We compared harmonization quality of these two kinds of models using 20 imaging features quantifying T1 signal, contrast between brain structures and segmentation quality. Results illustrate the potential of 3D CycleGAN for better synthesize images in inter-scanner MRI harmonization tasks.
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