ODE-based Deep Network for MRI Reconstruction
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Image ReconstructionMRI ReconstructionSimilar Papers 제목 키워드 기반
Randomized Iterative Reconstruction for Sparse View X-ray Computed Tomography
With the availability of more powerful computers, iterative reconstruction algorithms are the subject of an ongoing work in the design of more efficient reconstruction algorithms for X-ray computed tomography. In this wo…
General Phase Regularized Reconstruction using Phase Cycling
Purpose: To develop a general phase regularized image reconstruction method, with applications to partial Fourier imaging, water-fat imaging and flow imaging. Theory and Methods: The problem of enforcing phase constrai…
compressed sensingImage ReconstructionGuided Signal Reconstruction Theory
An axiomatic approach to signal reconstruction is formulated, involving a sample consistent set and a guiding set, describing desired reconstructions. New frame-less reconstruction methods are proposed, based on a novel …
Real-time Dynamic MRI Reconstruction using Stacked Denoising Autoencoder
In this work we address the problem of real-time dynamic MRI reconstruction. There are a handful of studies on this topic; these techniques are either based on compressed sensing or employ Kalman Filtering. These techniq…
compressed sensingDenoisingMRI ReconstructionWideRange4D: Enabling High-Quality 4D Reconstruction with Wide-Range Movements and Scenes
With the rapid development of 3D reconstruction technology, research in 4D reconstruction is also advancing, existing 4D reconstruction methods can generate high-quality 4D scenes. However, due to the challenges in acqui…
3D Reconstruction4D reconstruction