3D conditional GAN with transfer learning for pediatric MR to CT image synthesis combining adult and pediatric patient data

CT image synthesis from MR images is necessary for MR-only treatment planning, MRI-based quality assurance (QA), and treatment assessment in radiation therapy (RT). For pediatric cancer patients, reducing ionizing radiation from CT scans is preferred for which MRI-based RT planning and assessment are truly beneficial. Recently, deep learning-based synthetic CT (sCT) generation have demonstrated promising results on adult data. Generally, it is challenging to develop a pediatric sCT generation model due to significant anatomical variability and relatively smaller number of available pediatric data compared to adult. In this study, we investigated a 3D conditional generative adversarial network (cGAN)-based transfer learning approach for accurate pediatric sCT generation. Our model was first trained using adult data with augmentation by scaling to simulate pediatric data, followed by fine-Tuning on pediatric data. We compared three different training scenarios; (1) training on 50 adult patient data with scaling augmentation, (2) training on combined 50 adult and 50 pediatric patient data, and (3) fine-Tuning on 50 pediatric data using the pre-Trained model on 50 adult data. 3D cGAN with transfer learning showed significantly better synthesis performance than the other models with average mean absolute error (MAE), peak signal-To-noise ratio (PSNR), and structural similarity (SSIM) index of 51.99 HU, 24.74, and 0.80, respectively. The proposed 3D cGAN-based transfer learning was able to accurately synthesize pediatric CT images from MRI, allowing us to realize pediatric MR-only RT planning, QA, and treatment assessment.