The use of deep learning methods in low-dose computed tomography image reconstruction: a systematic review
Conventional reconstruction techniques, such as filtered back projection (FBP) and iterative
reconstruction (IR), which have been utilised widely in the image reconstruction process of …
reconstruction (IR), which have been utilised widely in the image reconstruction process of …
A review of deep learning methods for compressed sensing image reconstruction and its medical applications
Compressed sensing (CS) and its medical applications are active areas of research. In this
paper, we review recent works using deep learning method to solve CS problem for images …
paper, we review recent works using deep learning method to solve CS problem for images …
CD-Net: Comprehensive domain network with spectral complementary for DECT sparse-view reconstruction
Dual-energy computed tomography (DECT) is of great clinical significance because of its
material identification and quantification capacity. Although DECT measures attenuation …
material identification and quantification capacity. Although DECT measures attenuation …
Regularization strategies in statistical image reconstruction of low‐dose x‐ray CT: A review
Statistical image reconstruction (SIR) methods have shown potential to substantially improve
the image quality of low‐dose x‐ray computed tomography (CT) as compared to the …
the image quality of low‐dose x‐ray computed tomography (CT) as compared to the …
[HTML][HTML] ADAPTIVE-NET: deep computed tomography reconstruction network with analytical domain transformation knowledge
Background Recently, the paradigm of computed tomography (CT) reconstruction has
shifted as the deep learning technique evolves. In this study, we proposed a new …
shifted as the deep learning technique evolves. In this study, we proposed a new …
Data and image prior integration for image reconstruction using consensus equilibrium
Image domain prior models have been shown to improve the quality of reconstructed
images, especially when data are limited. Pre-processing of raw data, through the implicit or …
images, especially when data are limited. Pre-processing of raw data, through the implicit or …
实现稀疏角度下的精确CT 重建: 利用ADMM-LP 算法求解非凸模型
宋洁， 陈平， 潘晋孝 - 中国组织工程研究, 2018 - cjter.com
实现稀疏角度下的精确CT重建:利用ADMM-LP算法求解非凸模型 Page 1 《中国组织工程研究》
Chinese Journal of Tissue Engineering Research 文章编号:2095-4344(2018)31-04998-05 …
Chinese Journal of Tissue Engineering Research 文章编号:2095-4344(2018)31-04998-05 …
Dual-domain projection fidelity network for sparse-view helical CT reconstruction
Z Mao, Y Wang, G Chen, M Zhu… - … 2023: Physics of …, 2023 - spiedigitallibrary.org
Sparse-view computed tomographic (CT) image reconstruction aims to shorten scanning
time, reduce radiation dose, and yield high-quality CT images simultaneously. Some …
time, reduce radiation dose, and yield high-quality CT images simultaneously. Some …
Reconstruction accuracy of sparse angle CT imaging: ADMM-CT algorithm based on LP-norm
J Song, P Chen, J Pan - Chinese Journal of Tissue Engineering Research, 2018 - cjter.com
BACKGROUND: Sparse-view CT imaging reconstruction is an effective method for reducing
radiation dosage. But the reconstruction accuracy affects its promotion in clinic. OBJECTIVE …
radiation dosage. But the reconstruction accuracy affects its promotion in clinic. OBJECTIVE …
Statistical CT reconstruction using region-aware texture preserving regularization learning from prior normal-dose CT image
In some clinical applications, prior normal-dose CT (NdCT) images are available, and the
valuable textures and structure features in them may be used to promote follow-up low-dose …
valuable textures and structure features in them may be used to promote follow-up low-dose …