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Deep learning based spectral CT imaging
Spectral computed tomography (CT) has attracted much attention in radiation dose
reduction, metal artifacts removal, tissue quantification and material discrimination. The x-ray …
reduction, metal artifacts removal, tissue quantification and material discrimination. The x-ray …
Non-local low-rank cube-based tensor factorization for spectral CT reconstruction
Spectral computed tomography (CT) reconstructs material-dependent attenuation images
from the projections of multiple narrow energy windows, which is meaningful for material …
from the projections of multiple narrow energy windows, which is meaningful for material …
Feasibility study of three-material decomposition in dual-energy cone-beam CT imaging with deep learning
J Zhu, T Su, X Zhang, J Yang, D Mi… - Physics in Medicine …, 2022 - iopscience.iop.org
Objective. In this work, a dedicated end-to-end deep convolutional neural network, named
as Triple-CBCT, is proposed to demonstrate the feasibility of reconstructing three different …
as Triple-CBCT, is proposed to demonstrate the feasibility of reconstructing three different …
SISTER: Spectral-image similarity-based tensor with enhanced-sparsity reconstruction for sparse-view multi-energy CT
Multi-energy computed tomography (MCT) has a great potential in material decomposition,
tissue characterization, lesion detection, and other applications. However, the severe noise …
tissue characterization, lesion detection, and other applications. However, the severe noise …
DIRECT‐Net: a unified mutual‐domain material decomposition network for quantitative dual‐energy CT imaging
Purpose The purpose of this paper is to present an end‐to‐end deep convolutional neural
network to improve the dual‐energy CT (DECT) material decomposition performance …
network to improve the dual‐energy CT (DECT) material decomposition performance …
A high-quality photon-counting CT technique based on weight adaptive total-variation and image-spectral tensor factorization for small animals imaging
Photon-counting X-ray computed tomography (CT) has been attracting great attention in
tissue characterization, material discrimination, and so on. The emitting X-ray energy …
tissue characterization, material discrimination, and so on. The emitting X-ray energy …
Image restoration for low-dose CT via transfer learning and residual network
A Zhong, B Li, N Luo, Y Xu, L Zhou, X Zhen - IEEE Access, 2020 - ieeexplore.ieee.org
Deep learning has recently been extensively investigated to remove artifacts in low-dose
computed tomography (LDCT). However, the power of transfer learning for medical image …
computed tomography (LDCT). However, the power of transfer learning for medical image …
FONT-SIR: Fourth-order nonlocal tensor decomposition model for spectral CT image reconstruction
X Chen, W **a, Y Liu, H Chen, J Zhou… - … on Medical Imaging, 2022 - ieeexplore.ieee.org
Spectral computed tomography (CT) reconstructs images from different spectral data
through photon counting detectors (PCDs). However, due to the limited number of photons …
through photon counting detectors (PCDs). However, due to the limited number of photons …
DECT sparse reconstruction based on hybrid spectrum data generative diffusion model
Purpose Dual-energy computed tomography (DECT) enables the differentiation of different
materials. Additionally, DECT images consist of multiple scans of the same sample …
materials. Additionally, DECT images consist of multiple scans of the same sample …
Spectral CT reconstruction via spectral-image tensor and bidirectional image-gradient minimization
It is challenging to obtain good image quality in spectral computed tomography (CT) as the
photon-number for the photon-counting detectors is limited for each narrow energy bin. This …
photon-number for the photon-counting detectors is limited for each narrow energy bin. This …