LEARN: Learned experts' assessment-based reconstruction network for sparse-data CT
Compressive sensing (CS) has proved effective for tomographic reconstruction from
sparsely collected data or under-sampled measurements, which are practically important for …
sparsely collected data or under-sampled measurements, which are practically important for …
MAGIC: Manifold and graph integrative convolutional network for low-dose CT reconstruction
Low-dose computed tomography (LDCT) scans, which can effectively alleviate the radiation
problem, will degrade the imaging quality. In this paper, we propose a novel LDCT …
problem, will degrade the imaging quality. In this paper, we propose a novel LDCT …
CT reconstruction with PDF: Parameter-dependent framework for data from multiple geometries and dose levels
The current mainstream computed tomography (CT) reconstruction methods based on deep
learning usually need to fix the scanning geometry and dose level, which significantly …
learning usually need to fix the scanning geometry and dose level, which significantly …
PWLS-ULTRA: An efficient clustering and learning-based approach for low-dose 3D CT image reconstruction
The development of computed tomography (CT) image reconstruction methods that
significantly reduce patient radiation exposure, while maintaining high image quality is an …
significantly reduce patient radiation exposure, while maintaining high image quality is an …
Convergent regularization in inverse problems and linear plug-and-play denoisers
Regularization is necessary when solving inverse problems to ensure the well-posedness of
the solution map. Additionally, it is desired that the chosen regularization strategy is …
the solution map. Additionally, it is desired that the chosen regularization strategy is …
Physics-/model-based and data-driven methods for low-dose computed tomography: A survey
Since 2016, deep learning (DL) has advanced tomographic imaging with remarkable
successes, especially in low-dose computed tomography (LDCT) imaging. Despite being …
successes, especially in low-dose computed tomography (LDCT) imaging. Despite being …
Patch-based denoising diffusion probabilistic model for sparse-view CT reconstruction
Sparse-view computed tomography (CT) can be used to reduce radiation dose greatly but is
suffers from severe image artifacts. Recently, the deep learning based method for sparse …
suffers from severe image artifacts. Recently, the deep learning based method for sparse …
Generative modeling in sinogram domain for sparse-view CT reconstruction
B Guan, C Yang, L Zhang, S Niu… - … on Radiation and …, 2023 - ieeexplore.ieee.org
The radiation dose in computed tomography (CT) examinations is harmful for patients but
can be significantly reduced by intuitively decreasing the number of projection views …
can be significantly reduced by intuitively decreasing the number of projection views …
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 …
Sparse-view cone beam CT reconstruction using data-consistent supervised and adversarial learning from scarce training data
Reconstruction of CT images from a limited set of projections through an object is important
in several applications ranging from medical imaging to industrial settings. As the number of …
in several applications ranging from medical imaging to industrial settings. As the number of …