# A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction

## TL;DR

This paper introduces a deep cascade of convolutional neural networks for MRI reconstruction from undersampled data, significantly improving speed and accuracy over traditional compressed sensing methods, enabling real-time imaging.

## Contribution

The paper presents a novel deep learning framework that outperforms existing methods in MRI reconstruction speed and quality, especially for highly undersampled data.

## Key findings

- Outperforms compressed sensing methods in reconstruction error and perceptual quality.
- Reconstructs images in 23 ms, enabling real-time applications.
- Produces approximately half the error of previous state-of-the-art methods.

## Abstract

The acquisition of Magnetic Resonance Imaging (MRI) is inherently slow. Inspired by recent advances in deep learning, we propose a framework for reconstructing MR images from undersampled data using a deep cascade of convolutional neural networks to accelerate the data acquisition process. We show that for Cartesian undersampling of 2D cardiac MR images, the proposed method outperforms the state-of-the-art compressed sensing approaches, such as dictionary learning-based MRI (DLMRI) reconstruction, in terms of reconstruction error, perceptual quality and reconstruction speed for both 3-fold and 6-fold undersampling. Compared to DLMRI, the error produced by the method proposed is approximately twice as small, allowing to preserve anatomical structures more faithfully. Using our method, each image can be reconstructed in 23 ms, which is fast enough to enable real-time applications.