IDPCNN: Iterative denoising and projecting CNN for MRI reconstruction

Compressed sensing magnetic resonance imaging (CS-MRI) makes it possible to shorten data acquisition time substantially. The traditional iteration-based CS-MRI method is flexible in modeling but is usually time-consuming. Recently, the deep neural network method becomes popular in CS-MRI due to its high efficiency. However, the drawback of the deep learning method is inflexibility. It depends overly on the training images and scanning method of the k-space data. In this paper, we propose an iterative method for MRI reconstruction, called IDPCNN, combining the merits of both the traditional method and the deep learning methods, realizing quick, flexible, and accurate reconstruction. The proposed method incorporates two stages: denoising and projection. The denoising step employs a state-of-the-art denoiser to smooth the image. The projection step explores the prior information from the frequency domain and adds details to the spatial domain iteratively. The reconstruction quality is superior to the best MRI reconstruction methods under different sampling masks and rates. The stability, speed, and good reconstruction quality mean that our IDPCNN has the potential for widespread clinical applications. (C)& nbsp;2021 Elsevier B.V. All rights reserved.

Automated summary

This paper proposes an iterative MRI reconstruction method, called IDPCNN, that combines the advantages of traditional methods and deep learning methods to achieve fast, flexible, and accurate reconstruction. The method includes two stages, denoising and projection, and outperforms existing methods in terms of reconstruction quality, with great potential for widespread clinical applications.