Ultra-low-dose spectral CT based on a multi-level wavelet convolutional neural network
M Lee, H Kim, HM Cho, HJ Kim - Journal of Digital Imaging, 2021 - Springer
Spectral computed tomography (CT) based on a photon-counting detector (PCD) is a
promising technique with the potential to improve lesion detection, tissue characterization …
promising technique with the potential to improve lesion detection, tissue characterization …
[PDF][PDF] Learned experts' assessment-based reconstruction network (” learn”) for sparse-data ct,”
H Chen, Y Zhang, W Zhang, H Sun, P Liao… - arXiv preprint arXiv …, 2017 - researchgate.net
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 …
Improving the quality of sparse-view cone-beam computed tomography via reconstruction-friendly interpolation network
Y Wang, L Chao, W Shan, H Zhang… - Proceedings of the …, 2022 - openaccess.thecvf.com
Reconstructing cone-beam computed tomography (CBCT) typically utilizes a Feldkamp-
Davis-Kress (FDK) algorithm to'translate'hundreds of 2D X-ray projections on different …
Davis-Kress (FDK) algorithm to'translate'hundreds of 2D X-ray projections on different …
A U-Nets cascade for sparse view computed tomography
We propose a new convolutional neural network architecture for image reconstruction in
sparse view computed tomography. The proposed network consists of a cascade of U-nets …
sparse view computed tomography. The proposed network consists of a cascade of U-nets …
[HTML][HTML] A deep learning reconstruction framework for X-ray computed tomography with incomplete data
As a powerful imaging tool, X-ray computed tomography (CT) allows us to investigate the
inner structures of specimens in a quantitative and nondestructive way. Limited by the …
inner structures of specimens in a quantitative and nondestructive way. Limited by the …
A deep convolutional neural network using directional wavelets for low‐dose X‐ray CT reconstruction
Purpose Due to the potential risk of inducing cancer, radiation exposure by X‐ray CT
devices should be reduced for routine patient scanning. However, in low‐dose X‐ray CT …
devices should be reduced for routine patient scanning. However, in low‐dose X‐ray CT …
Snaf: Sparse-view cbct reconstruction with neural attenuation fields
Cone beam computed tomography (CBCT) has been widely used in clinical practice,
especially in dental clinics, while the radiation dose of X-rays when capturing has been a …
especially in dental clinics, while the radiation dose of X-rays when capturing has been a …
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 …
Robust X-ray sparse-view phase tomography via hierarchical synthesis convolutional neural networks
Convolutional Neural Networks (CNN) based image reconstruction methods have been
intensely used for X-ray computed tomography (CT) reconstruction applications. Despite …
intensely used for X-ray computed tomography (CT) reconstruction applications. Despite …
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 …