ODE-based Deep Network for MRI Reconstruction
- URL: http://arxiv.org/abs/1912.12325v1
- Date: Fri, 27 Dec 2019 20:13:30 GMT
- Title: ODE-based Deep Network for MRI Reconstruction
- Authors: Ali Pour Yazdanpanah, Onur Afacan, Simon K. Warfield
- Abstract summary: 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.
- Score: 1.569044447685249
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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