# ODE-based Deep Network for MRI Reconstruction

arXiv: https://arxiv.org/abs/1912.12325 · HTML: https://mlsanity.com/paper/1912.12325/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Dec 2019.

## Built on by (most cited first, 1 of 1)

- [Differential Motion Evolution for Fine-Grained Motion Deformation in Unsupervised Image Animation](https://mlsanity.com/paper/2110.04658/) (2021)

## Abstract (arXiv)

Fast data acquisition in Magnetic Resonance Imaging (MRI) is vastly in demand and scan time directly depends on the number of acquired k-space samples. The data-driven methods based on deep neural networks have resulted in promising improvements, compared to the conventional methods, in image reconstruction algorithms. The connection between deep neural network and Ordinary Differential Equation (ODE) has been observed and studied recently. The studies show that different residual networks can be interpreted as Euler discretization of an ODE. In this paper, we propose an ODE-based deep network for MRI reconstruction to enable the rapid acquisition of MR images with improved image quality. Our results with undersampled data demonstrate that our method can deliver higher quality images in comparison to the reconstruction methods based on the standard UNet network and Residual network.