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 …
Momentum-Net: Fast and convergent iterative neural network for inverse problems
Iterative neural networks (INN) are rapidly gaining attention for solving inverse problems in
imaging, image processing, and computer vision. INNs combine regression NNs and an …
imaging, image processing, and computer vision. INNs combine regression NNs and an …
Improved low-count quantitative PET reconstruction with an iterative neural network
Image reconstruction in low-count PET is particularly challenging because gammas from
natural radioactivity in Lu-based crystals cause high random fractions that lower the …
natural radioactivity in Lu-based crystals cause high random fractions that lower the …
Review of sparse-view or limited-angle CT reconstruction based on deep learning
J Di, J Lin, L Zhong, K Qian, Y Qin - Laser & Optoelectronics …, 2023 - researching.cn
Computed tomography (CT) technology is widely used in clinical medical diagnosis thanks
to the excellent visualization of the CT imaging technology for the internal cross-sectional …
to the excellent visualization of the CT imaging technology for the internal cross-sectional …
Convolutional analysis operator learning: Acceleration and convergence
IY Chun, JA Fessler - IEEE Transactions on Image Processing, 2019 - ieeexplore.ieee.org
Convolutional operator learning is gaining attention in many signal processing and
computer vision applications. Learning kernels has mostly relied on so-called patch-domain …
computer vision applications. Learning kernels has mostly relied on so-called patch-domain …
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 …
Data and image prior integration for image reconstruction using consensus equilibrium
Image domain prior models have been shown to improve the quality of reconstructed
images, especially when data are limited. Pre-processing of raw data, through the implicit or …
images, especially when data are limited. Pre-processing of raw data, through the implicit or …
Convolutional analysis operator learning: Dependence on training data
Convolutional analysis operator learning (CAOL) enables the unsupervised training of
(hierarchical) convolutional sparsifying operators or autoencoders from large datasets. One …
(hierarchical) convolutional sparsifying operators or autoencoders from large datasets. One …
Convolutional analysis operator learning for multifocus image fusion
C Zhang, Z Feng - Signal Processing: Image Communication, 2022 - Elsevier
Sparse representation (SR), convolutional sparse representation (CSR) and convolutional
dictionary learning (CDL) are synthetic-based priors that have proven to be successful in …
dictionary learning (CDL) are synthetic-based priors that have proven to be successful in …
Sparse-View X-Ray CT Reconstruction Using Prior with Learned Transform
A major challenge in X-ray computed tomography (CT) is reducing radiation dose while
maintaining high quality of reconstructed images. To reduce the radiation dose, one can …
maintaining high quality of reconstructed images. To reduce the radiation dose, one can …