A DNN model for image reconstruction from subsampled MRI scans. It can also be used for image denoising and super-resolution. However, not all image properties are explicitly exploited.
A deep learning framework for MR image reconstruction called AUTOMAP. It is accurate when compared to conventional methods. However, it is computationally intensive.
A model that blends variational model mathematics with deep learning. Standard reconstruction techniques are outperformed by the model. There is further work to be done on several types of error measures.
For rapid and accurate CS-MRI reconstruction, a deep learning model has been developed. There is still a requirement to comprehend the proposed method’s design.
A method for image reconstruction denoising and data integrity enforcement. Due to a decrease in trainable parameters, it does not need a large amount of training data.
A deep neural network-based image reconstruction model. The computational difficulty of compressed sensing-based approaches was addressed in the model.