IR-FRestormer: Iterative Refinement With Fourier-Based Restormer for Accelerated MRI Reconstruction

Accelerated magnetic resonance imaging (MRI) aims to reconstruct high-quality MR images from a set of under-sampled measurements. State-of-the-art methods for this task use deep learning, which offers high reconstruction accuracy and fast runtimes. In this work, we propose a new state-of-the-art reconstruction model for accelerated MRI reconstruction. Our model is the first to combine the power of deep neural networks with iterative refinement for this task. For the neural network component of our method, we utilize a transformer-based architecture as transformers are state-of-the-art in various image reconstruction tasks. However, a major drawback of transformers which has limited their emergence among the state-of-the-art MRI models is that they are often memory inefficient for high-resolution inputs. To address this limitation, we propose a transformer-based model which uses parameter-free Fourier-based attention modules, achieving 2x more memory efficiency. We evaluate our model on the largest publicly available MRI dataset, the fastMRI dataset, and achieve on-par performance with other state-of-the-art methods on the dataset's leaderboard.