Improved Deep Learning MR Image Enhancement with Synthetic Images

Motivation: Deep learning (DL) based image enhancement requires paired data for supervised training. But separately acquired data pairs may encounter spatial mis-alignment that limits the model performance. Goal(s): Incorporate synthetic data into the training set to address the mis-alignment issue and improve the quality and diversity of the training set. Approach: Develop and validate the diffusion based image degrader to synthesize low quality images. Compare the performance of DL models trained with/without synthetic data. Results: DL models trained with synthetic data can achieve similar performance compared to training with acquired pairs. Additional synthetic data can improve DL image enhancement. Impact: Synthetic data allows building more diverse training sets to achieve multi-task DL models. How much faster the DL model can support and whether it can control the quality of output to meet different clinical preferences is worth further investigation.