Translation of computed tomography images to T2-Weighted magnetic resonance images of lumbar spine using generative adversarial networks on sagittal images

This study aims to develop a generative adversarial networks (GAN)-based image translation model for synthesizing lumbar spine Computed Tomography (CT) to Magnetic Resonance (MR) images, focusing on sagittal images, and to evaluate its performance. A cycle-consistent GAN was used to translate lumbar spine CT slices into synthetic T2-weighted MR images. The model was trained on a dataset of 100 cases with co-registered CT and MR images in the sagittal plane from patients with degenerative disease. A qualitative analysis was performed with 30 cases, using a similarity score to evaluate anatomical features by neurosurgeons. Quantitative metrics, including mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM), were also computed. The GAN model successfully generated synthetic T2-weighted MR images that visually resembled real MR images. In qualitative evaluation, the similarity score for anatomical features (e.g., disc signal, paraspinal muscles, facet joints) averaged over 80%. The disc signal showed the highest similarity at 88.11% ± 4.47%. In the quantitative assessment of sagittal images, the results were: MAE = 43.32 ± 10.29, PSNR = 12.80 ± 1.55, and SSIM = 0.28 ± 0.07. This approach could be valuable in clinical settings where MR image is unavailable, potentially reducing healthcare costs.