Revolutionizing Brain Tumor Imaging: Generating Synthetic 3D FA Maps from T1-Weighted MRI using CycleGAN Models

Fractional anisotropy (FA) and direction-ally encoded colour (DEC) maps are essential for evaluating white matter integrity and structural connectivity in neu-roimaging. However, the spatial misalignment between FA maps and tractog-raphy atlases hinders their effective integration into predictive models. To address this issue, we propose a CycleGAN-based approach for generating FA and DEC maps directly from T1-weighted MRI scans, representing the first application of this technique to both healthy and tumor-affected tissues. Our model, trained on unpaired data, produces high-fidelity maps, which have been rigorously evaluated using Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR), demonstrating particularly robust performance in tumour regions. Radiological assessments further underscore the model's potential to enhance clinical workflows by providing an AI-driven alternative that reduces the necessity for additional scans.