CNN-based projected gradient descent for consistent CT image reconstruction
We present a new image reconstruction method that replaces the projector in a projected
gradient descent (PGD) with a convolutional neural network (CNN). Recently, CNNs trained …
gradient descent (PGD) with a convolutional neural network (CNN). Recently, CNNs trained …
Image reconstruction: From sparsity to data-adaptive methods and machine learning
The field of medical image reconstruction has seen roughly four types of methods. The first
type tended to be analytical methods, such as filtered backprojection (FBP) for X-ray …
type tended to be analytical methods, such as filtered backprojection (FBP) for X-ray …
Transform learning for magnetic resonance image reconstruction: From model-based learning to building neural networks
Magnetic resonance imaging (MRI) is widely used in clinical practice, but it has been
traditionally limited by its slow data acquisition. Recent advances in compressed sensing …
traditionally limited by its slow data acquisition. Recent advances in compressed sensing …
Noise2Recon: Enabling SNR‐robust MRI reconstruction with semi‐supervised and self‐supervised learning
Purpose To develop a method for building MRI reconstruction neural networks robust to
changes in signal‐to‐noise ratio (SNR) and trainable with a limited number of fully sampled …
changes in signal‐to‐noise ratio (SNR) and trainable with a limited number of fully sampled …
Wasserstein GANs for MR imaging: from paired to unpaired training
Lack of ground-truth MR images impedes the common supervised training of neural
networks for image reconstruction. To cope with this challenge, this article leverages …
networks for image reconstruction. To cope with this challenge, this article leverages …
Convolutional dictionary learning: Acceleration and convergence
IY Chun, JA Fessler - IEEE Transactions on Image Processing, 2017 - ieeexplore.ieee.org
Convolutional dictionary learning (CDL or sparsifying CDL) has many applications in image
processing and computer vision. There has been growing interest in developing efficient …
processing and computer vision. There has been growing interest in developing efficient …
Low-rank and adaptive sparse signal (LASSI) models for highly accelerated dynamic imaging
S Ravishankar, BE Moore… - IEEE transactions on …, 2017 - ieeexplore.ieee.org
Sparsity-based approaches have been popular in many applications in image processing
and imaging. Compressed sensing exploits the sparsity of images in a transform domain or …
and imaging. Compressed sensing exploits the sparsity of images in a transform domain or …
Homotopic gradients of generative density priors for MR image reconstruction
Deep learning, particularly the generative model, has demonstrated tremendous potential to
significantly speed up image reconstruction with reduced measurements recently. Rather …
significantly speed up image reconstruction with reduced measurements recently. Rather …
An -Divergence-Based Approach for Robust Dictionary Learning
A Iqbal, AK Seghouane - IEEE Transactions on Image …, 2019 - ieeexplore.ieee.org
In this paper, a robust sequential dictionary learning (DL) algorithm is presented. The
proposed algorithm is motivated from the maximum likelihood perspective on dictionary …
proposed algorithm is motivated from the maximum likelihood perspective on dictionary …
Optimization methods for MR image reconstruction (long version)
JA Fessler - arXiv preprint arXiv:1903.03510, 2019 - arxiv.org
The development of compressed sensing methods for magnetic resonance (MR) image
reconstruction led to an explosion of research on models and optimization algorithms for MR …
reconstruction led to an explosion of research on models and optimization algorithms for MR …