DYNAMIC CHANNEL-ALIGNED 3D U-NET FOR EXPLAINABLE T2-WEIGHTED INFANT BRAIN MRI SYNTHESIS FROM T1-WEIGHTED MRI

Magnetic Resonance Imaging (MRI) is a crucial tool in clinical diagnostics, with T1-weighted (T1) and T2-weighted (T2). Acquiring high-quality T2-weighted MRI, especially for infant brains, presents challenges due to lengthy acquisition times, motion artifacts, and scanner variability. This study introduces the Adaptive Dual Domain U-Net, a novel 3D U-Net architecture enhanced with dynamic channel alignment for synthesizing T2-weighted MRI from T1-weighted inputs. The proposed model addresses domain variability, integrates explainability tools using Captum, and employs patch-based training for efficient memory utilization and high-resolution reconstruction. Quantitative evaluations on the iSeg-2019 dataset demonstrate superior performance across key metrics such as Mean Squared Error (MSE), Structural Similarity Index (SSIM), and R² compared to baseline methods. Qualitative results highlight the model’s ability to generate structurally accurate and clinically interpretable synthetic T2- weighted images, making it a robust tool for both clinical and research applications.