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

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.

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.

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.

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.

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.