Domain progressive 3D residual convolution network to improve low-dose CT imaging
The wide applications of X-ray computed tomography (CT) bring low-dose CT (LDCT) into a
clinical prerequisite, but reducing the radiation exposure in CT often leads to significantly …
clinical prerequisite, but reducing the radiation exposure in CT often leads to significantly …
A review of deep learning ct reconstruction from incomplete projection data
Computed tomography (CT) is a widely used imaging technique in both medical and
industrial applications. However, accurate CT reconstruction requires complete projection …
industrial applications. However, accurate CT reconstruction requires complete projection …
CT artifact correction for sparse and truncated projection data using generative adversarial networks
AR Podgorsak, MM Shiraz Bhurwani… - Medical Physics, 2021 - Wiley Online Library
Purpose Computed tomography image reconstruction using truncated or sparsely acquired
projection data to reduce radiation dose, iodine volume, and patient motion artifacts has …
projection data to reduce radiation dose, iodine volume, and patient motion artifacts has …
WNet: A data-driven dual-domain denoising model for sparse-view computed tomography with a trainable reconstruction layer
T Cheslerean-Boghiu, FC Hofmann… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Deep learning based solutions are being succesfully implemented for a wide variety of
applications. Most notably, clinical use-cases have gained an increased interest and have …
applications. Most notably, clinical use-cases have gained an increased interest and have …
Promising generative adversarial network based sinogram inpainting method for ultra-limited-angle computed tomography imaging
Z Li, A Cai, L Wang, W Zhang, C Tang, L Li, N Liang… - Sensors, 2019 - mdpi.com
Limited-angle computed tomography (CT) image reconstruction is a challenging problem in
the field of CT imaging. In some special applications, limited by the geometric space and …
the field of CT imaging. In some special applications, limited by the geometric space and …
A dual-domain deep learning-based reconstruction method for fully 3D sparse data helical CT
Helical CT has been widely used in clinical diagnosis. In this work, we focus on a new
prototype of helical CT, equipped with sparsely spaced multidetector and multi-slit collimator …
prototype of helical CT, equipped with sparsely spaced multidetector and multi-slit collimator …
Learning to reconstruct CT images from the VVBP-tensor
Deep learning (DL) is bringing a big movement in the field of computed tomography (CT)
imaging. In general, DL for CT imaging can be applied by processing the projection or the …
imaging. In general, DL for CT imaging can be applied by processing the projection or the …
基于深度学习的稀疏或有限角度CT 重建方法研究综述
邸江磊， 林俊成， 钟丽云， 钱克矛… - Laser & Optoelectronics …, 2023 - opticsjournal.net
摘要由于计算机断层扫描(CT) 成像技术对物体内部结构具有出色的可视化能力,
其在临床医学诊断中获得广泛应用. 但是X 射线辐射会对人体造成伤害, 通常采用降低扫描强度 …
其在临床医学诊断中获得广泛应用. 但是X 射线辐射会对人体造成伤害, 通常采用降低扫描强度 …
Convolutional neural network–based metal and streak artifacts reduction in dental CT images with sparse‐view sampling scheme
Purpose Sparse‐view sampling has attracted attention for reducing the scan time and
radiation dose of dental cone‐beam computed tomography (CBCT). Recently, various deep …
radiation dose of dental cone‐beam computed tomography (CBCT). Recently, various deep …
Removing ring artefacts for photon-counting detectors using neural networks in different domains
The development of energy-resolving photon-counting detectors provides a new approach
for obtaining spectral information in computed tomography. However, the responses of …
for obtaining spectral information in computed tomography. However, the responses of …