Iterative low-dose CT reconstruction with priors trained by artificial neural network
Dose reduction in computed tomography (CT) is essential for decreasing radiation risk in
clinical applications. Iterative reconstruction algorithms are one of the most promising way to …
clinical applications. Iterative reconstruction algorithms are one of the most promising way to …
Image prediction for limited-angle tomography via deep learning with convolutional neural network
Limited angle problem is a challenging issue in x-ray computed tomography (CT) field.
Iterative reconstruction methods that utilize the additional prior can suppress artifacts and …
Iterative reconstruction methods that utilize the additional prior can suppress artifacts and …
Computationally efficient deep neural network for computed tomography image reconstruction
Purpose Deep neural network‐based image reconstruction has demonstrated promising
performance in medical imaging for undersampled and low‐dose scenarios. However, it …
performance in medical imaging for undersampled and low‐dose scenarios. However, it …
Artifact removal using a hybrid-domain convolutional neural network for limited-angle computed tomography imaging
The suppression of streak artifacts in computed tomography with a limited-angle
configuration is challenging. Conventional analytical algorithms, such as filtered …
configuration is challenging. Conventional analytical algorithms, such as filtered …
PIE-ARNet: Prior image enhanced artifact removal network for limited-angle DECT
Dual-energy computed tomography (DECT) is of great clinical significance because it can
simultaneously visualize the internal structure of the scanned object and provide material …
simultaneously visualize the internal structure of the scanned object and provide material …
Considering anatomical prior information for low-dose CT image enhancement using attribute-augmented Wasserstein generative adversarial networks
Currently, many deep learning (DL)-based low-dose CT image postprocessing technologies
fail to consider the anatomical differences in training data among different human body sites …
fail to consider the anatomical differences in training data among different human body sites …
ℓ0 Gradient Minimization Based Image Reconstruction for Limited-Angle Computed Tomography
W Yu, L Zeng - PloS one, 2015 - journals.plos.org
In medical and industrial applications of computed tomography (CT) imaging, limited by the
scanning environment and the risk of excessive X-ray radiation exposure imposed to the …
scanning environment and the risk of excessive X-ray radiation exposure imposed to the …
Compressive sensing for direct millimeter-wave holographic imaging
Direct millimeter-wave (MMW) holographic imaging, which provides both the amplitude and
phase information by using the heterodyne mixing technique, is considered a powerful tool …
phase information by using the heterodyne mixing technique, is considered a powerful tool …
Low-dose lung CT image restoration using adaptive prior features from full-dose training database
Y Zhang, J Rong, H Lu, Y Xing… - IEEE transactions on …, 2017 - ieeexplore.ieee.org
The valuable structure features in full-dose computed tomography (FdCT) scans can be
exploited as prior knowledge for low-dose CT (LdCT) imaging. However, lacking the …
exploited as prior knowledge for low-dose CT (LdCT) imaging. However, lacking the …
Prior‐based artifact correction (PBAC) in computed tomography
Purpose: Image quality in computed tomography (CT) often suffers from artifacts which may
reduce the diagnostic value of the image. In many cases, these artifacts result from missing …
reduce the diagnostic value of the image. In many cases, these artifacts result from missing …