Data-driven approach for task-driven medical image reconstruction and analysis - Li, Changhao (Nanyang Technological University, 2020)
Deep learning is an important part of artificial intelligence, where the neural network can be an efficient way to solve complex problems, especially in the field of computer vision. Therefore, it is benefited to be applied in medical image processing. In this report, we mainly talk about deep MRI reconstruction. Base on the compressed sensing magnetic resonance imaging and data-driven method, it benefits patients a lot since the time of MRI acquisition can be largely reduced which only demands k-spaced data with low under sampled rate. Recently, deep learning based method on MRI reconstruction become more popular than traditional approaches. However, most of reconstruction models may not have good generalization performance, which means only perform well on a specific dataset. Thus, it is necessary to figure out how the different deep learning method work and also important to apply them on different dataset. In this report, we introduce the basic principle of deep learning, neural network and MRI reconstruction. Then we present how to apply them on MRI reconstruction. Finally, we would conduct some popular MRI reconstruction models which based on fully convolutional network or generative adversarial network (GAN) or cascaded network, and test atypical network on MRBrainS13 dataset which is not the original dataset of all the referred model in this report.