Fine-grained MRI Reconstruction using Attentive Selection Generative Adversarial Networks
This work addresses the need for fast and accurate MRI reconstruction in time-critical medical applications, representing an incremental improvement over existing deep learning approaches.
The paper tackles the problem of high-fidelity MRI reconstruction from compressed sensing data, especially at low sampling rates, by proposing an attention-based deep learning framework that achieves superior image quality compared to other deep learning methods.
Compressed sensing (CS) leverages the sparsity prior to provide the foundation for fast magnetic resonance imaging (fastMRI). However, iterative solvers for ill-posed problems hinder their adaption to time-critical applications. Moreover, such a prior can be neither rich to capture complicated anatomical structures nor applicable to meet the demand of high-fidelity reconstructions in modern MRI. Inspired by the state-of-the-art methods in image generation, we propose a novel attention-based deep learning framework to provide high-quality MRI reconstruction. We incorporate large-field contextual feature integration and attention selection in a generative adversarial network (GAN) framework. We demonstrate that the proposed model can produce superior results compared to other deep learning-based methods in terms of image quality, and relevance to the MRI reconstruction in an extremely low sampling rate diet.