Accelerated MRI with un-trained neural networks
MZ Darestani, R Heckel - IEEE Transactions on Computational …, 2021 - ieeexplore.ieee.org
Convolutional Neural Networks (CNNs) are highly effective for image reconstruction
problems. Typically, CNNs are trained on large amounts of training images. Recently …
problems. Typically, CNNs are trained on large amounts of training images. Recently …
A deep cascade of convolutional neural networks for dynamic MR image reconstruction
Inspired by recent advances in deep learning, we propose a framework for reconstructing
dynamic sequences of 2-D cardiac magnetic resonance (MR) images from undersampled …
dynamic sequences of 2-D cardiac magnetic resonance (MR) images from undersampled …
Reference-driven compressed sensing MR image reconstruction using deep convolutional neural networks without pre-training
D Zhao, F Zhao, Y Gan - Sensors, 2020 - mdpi.com
Deep learning has proven itself to be able to reduce the scanning time of Magnetic
Resonance Imaging (MRI) and to improve the image reconstruction quality since it was …
Resonance Imaging (MRI) and to improve the image reconstruction quality since it was …
IKWI-net: A cross-domain convolutional neural network for undersampled magnetic resonance image reconstruction
Z Wang, H Jiang, H Du, J Xu, B Qiu - Magnetic resonance imaging, 2020 - Elsevier
Magnetic resonance imaging (MRI) is widely used to get the information of anatomical
structure and physiological function with the advantages of high resolution and non-invasive …
structure and physiological function with the advantages of high resolution and non-invasive …
K-space and image domain collaborative energy-based model for parallel MRI reconstruction
Z Tu, C Jiang, Y Guan, J Liu, Q Liu - Magnetic Resonance Imaging, 2023 - Elsevier
Decreasing magnetic resonance (MR) image acquisition times can potentially make MR
examinations more accessible. Prior arts including the deep learning models have been …
examinations more accessible. Prior arts including the deep learning models have been …
Predictive uncertainty in deep learning–based MR image reconstruction using deep ensembles: Evaluation on the fastMRI data set
Purpose To estimate pixel‐wise predictive uncertainty for deep learning–based MR image
reconstruction and to examine the impact of domain shifts and architecture robustness …
reconstruction and to examine the impact of domain shifts and architecture robustness …
Universal undersampled mri reconstruction
Deep neural networks have been extensively studied for undersampled MRI reconstruction.
While achieving state-of-the-art performance, they are trained and deployed specifically for …
While achieving state-of-the-art performance, they are trained and deployed specifically for …
Cdiffmr: Can we replace the gaussian noise with k-space undersampling for fast MRI?
Deep learning has shown the capability to substantially accelerate MRI reconstruction while
acquiring fewer measurements. Recently, diffusion models have gained burgeoning …
acquiring fewer measurements. Recently, diffusion models have gained burgeoning …
Deep learning for undersampled MRI reconstruction
This paper presents a deep learning method for faster magnetic resonance imaging (MRI)
by reducing k-space data with sub-Nyquist sampling strategies and provides a rationale for …
by reducing k-space data with sub-Nyquist sampling strategies and provides a rationale for …
IDPCNN: Iterative denoising and projecting CNN for MRI reconstruction
Compressed sensing magnetic resonance imaging (CS-MRI) makes it possible to shorten
data acquisition time substantially. The traditional iteration-based CS-MRI method is flexible …
data acquisition time substantially. The traditional iteration-based CS-MRI method is flexible …