Aligning brain tumor imaging: generating synthetic 3D FA maps from T1-weighted MRI using CycleGAN models.

Objectives: This study addresses the issue of spatial misalignment between fractional anisotropy (FA) maps and tractography atlases in neuroimaging. We propose a CycleGAN-based method to generate FA and directionally encoded color (DEC) maps directly from T1-weighted MRI scans, applicable to both healthy and tumor-affected cases. Methods: A CycleGAN model was developed and trained using unpaired data to convert T1-weighted MRI images into FA and DEC maps. The quality of the generated maps was assessed using Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) metrics. Special emphasis was placed on evaluating performance in tumor regions. Radiological assessments were also conducted to determine the clinical applicability of the generated maps. Results: The CycleGAN-based model effectively generated high-fidelity FA and DEC maps, with robust SSIM and PSNR values, particularly in tumor regions. Radiological evaluations highlighted the potential of this approach to enhance clinical workflows, offering a reliable AI-driven alternative to traditional image processing methods. Conclusions: This CycleGAN-based approach successfully mitigates the problem of spatial misalignment by generating accurate FA and DEC maps from T1-weighted MRI scans. The method offers a promising pathway for integrating assessments of white matter integrity into predictive models without requiring additional imaging. Advances in knowledge: This study introduces the first use of CycleGAN for generating FA and DEC maps from T1-weighted images for tumor-affected cases, presenting a method that could enhance neuroimaging procedures by reducing the need for additional scans and improving efficiency in clinical settings.