Conditional GAN for Time-to-Peak (TTP) Generation from Non-Contrast MRI Modalities
Mariana Brejo, Hichem Maaref, Sadia Ahsan, Vincent Vigneron, Andreia Vasconcellos Faria · 2025
Magnetic resonance imaging (MRI) is essential for diagnosing acute ischemic stroke and assessing the extent of salvageable brain tissue, known as the ischemic penumbra. Perfusion-weighted imaging (PWI) provides crucial information for this assessment by allowing the calculation of the diffusionperfusion mismatch, a key factor in treatment choice. However, PWI is often unavailable in some clinical protocols due to logistical and technical constraints, such as administering an exogenous contrast agent. This work introduces a 3D conditional generative adversarial network (cGAN) designed to synthesize time-to-peak (TTP) perfusion maps from non-contrast MRI sequences. Our approach leverages spatial conditioning based on lesion annotations and employs a U-Net generator enhanced with attention mechanisms paired with a 3D PatchGAN discriminator. The quantitative evaluation shows that the model successfully generates high-fidelity TTP maps using apparent diffusion coefficient (ADC) and susceptibility-weighted imaging (SWI) as inputs, achieving structural similarity index measure (SSIM) scores as high as 0.89. These results demonstrate the promise of our method as a clinical decision-support tool and as an alternative for training models that estimate perfusion-related biomarkers, potentially overcoming the need for exogenous contrast in acute stroke evaluation.