Deep-neural-network-based sinogram synthesis for sparse-view CT image reconstruction

H Lee, J Lee, H Kim, B Cho… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
Recently, a number of approaches to low-dose computed tomography (CT) have been
developed and deployed in commercialized CT scanners. Tube current reduction is perhaps …

Sparse-view CT reconstruction based on multi-level wavelet convolution neural network

M Lee, H Kim, HJ Kim - Physica Medica, 2020 - Elsevier
Sparse-view computed tomography (CT) is a recent approach to reducing the radiation dose
in patients and speeding up the data acquisition. Consequently, sparse-view CT has been of …

Sinogram interpolation for sparse-view micro-CT with deep learning neural network

X Dong, S Vekhande, G Cao - Medical Imaging 2019: Physics …, 2019 - spiedigitallibrary.org
In sparse-view Computed Tomography (CT), only a small number of projection images are
taken around the object, and sinogram interpolation method has a significant impact on final …

LEARN: Learned experts' assessment-based reconstruction network for sparse-data CT

H Chen, Y Zhang, Y Chen, J Zhang… - IEEE transactions on …, 2018 - ieeexplore.ieee.org
Compressive sensing (CS) has proved effective for tomographic reconstruction from
sparsely collected data or under-sampled measurements, which are practically important for …

3D feature constrained reconstruction for low-dose CT imaging

J Liu, Y Hu, J Yang, Y Chen, H Shu… - … on Circuits and …, 2016 - ieeexplore.ieee.org
Low-dose computed tomography (LDCT) images are often highly degraded by amplified
mottle noise and streak artifacts. Maintaining image quality under low-dose scan protocols is …

View-interpolation of sparsely sampled sinogram using convolutional neural network

H Lee, J Lee, S Cho - Medical Imaging 2017: Image …, 2017 - spiedigitallibrary.org
Spare-view sampling and its associated iterative image reconstruction in computed
tomography have actively investigated. Sparse-view CT technique is a viable option to low …

High quality imaging from sparsely sampled computed tomography data with deep learning and wavelet transform in various domains

D Lee, S Choi, HJ Kim - Medical physics, 2019 - Wiley Online Library
Purpose Sparsely sampled computed tomography (CT) has been attracting attention as a
technique that can reduce the high radiation dose of conventional CT. In general, iterative …

A sparse-view CT reconstruction method based on combination of DenseNet and deconvolution

Z Zhang, X Liang, X Dong, Y Xie… - IEEE transactions on …, 2018 - ieeexplore.ieee.org
Sparse-view computed tomography (CT) holds great promise for speeding up data
acquisition and reducing radiation dose in CT scans. Recent advances in reconstruction …

Hybrid-domain neural network processing for sparse-view CT reconstruction

D Hu, J Liu, T Lv, Q Zhao, Y Zhang… - … on Radiation and …, 2020 - ieeexplore.ieee.org
X-ray computed tomography (CT) is one of the most widely used tools in medical imaging,
industrial nondestructive testing, lesion detection, and other applications. However …

[HTML][HTML] Artifact removal using improved GoogLeNet for sparse-view CT reconstruction

S Xie, X Zheng, Y Chen, L Xie, J Liu, Y Zhang, J Yan… - Scientific reports, 2018 - nature.com
Sparse-view Reconstruction can be used to provide accelerated low dose CT imaging with
both accelerated scan and reduced projection/back-projection calculation. Despite the rapid …