Accelerating cardiac cine MRI using a deep learning‐based ESPIRiT reconstruction
Magnetic Resonance in Medicine2020Vol. 85(1), pp. 152–167
Citations Over TimeTop 14% of 2020 papers
Abstract
DL-ESPIRiT synergistically combines a robust parallel imaging model and deep learning-based priors to produce high-fidelity reconstructions of retrospectively undersampled 2D cardiac cine data acquired with reduced FOV. Although a proof-of-concept is shown, further experiments are necessary to determine the efficacy of DL-ESPIRiT in prospectively undersampled data.
Related Papers
- → Single patient convolutional neural networks for real-time MR reconstruction: a proof of concept application in lung tumor segmentation for adaptive radiotherapy(2019)10 cited
- → Reconstruction of undersampled 3D non‐Cartesian image‐based navigators for coronary MRA using an unrolled deep learning model(2020)10 cited