Introduction: Magnetic Resonance Imaging is a commonly used technique for the initial diagnosis of gliomas. T1, T2, T2-FLAIR, and post-contrast T1 with gadolinium-based contrast agents (GBCAs) can show tumor characteristics. However, using this contrast agent poses a risk to patients with kidney failures, has environmental impact, and increases cost. To address these issues, we aimed to evaluate the potential of deep learning in generating synthetic post-contrast T1 images without using contrast agents.

Method: The project investigates the potential of using deep learning (DL) to generate synthetic post-contrast T1 images based on T1, T2, and T2-FLAIR provided by the Erasmus Glioma Database. Exploring different model architectures, loss functions, and input sizes to discover the optimal approach.

Results: Results show that individual loss functions, input size, and model complexity slightly impact the accuracy of synthetic post-contrast T1 images. Combining loss functions, however, was the most promising approach for image generation. Models trained with ℒm could generate low detail enhancement. Resulting in 0.0478±0.0076, 0.0139±0.0036, and 0.879±0.024 for MAE, MSE, and SSIM, respectively.

Conclusion: The study's findings indicate that DL is promising for generating synthetic post-contrast T1 images without using GBCAs. However, further research is required to generate realistic synthetic post-contrast T1 images. The study, however, provides a basis for future work and highlights the importance of reducing the use of GBCAs in clinical practice.