Medical image super-resolution reconstruction algorithms based on deep learning: A survey
D Qiu, Y Cheng, X Wang - Computer Methods and Programs in …, 2023 - Elsevier
Background and objective With the high-resolution (HR) requirements of medical images in
clinical practice, super-resolution (SR) reconstruction algorithms based on low-resolution …
clinical practice, super-resolution (SR) reconstruction algorithms based on low-resolution …
A review on deep learning approaches for low-dose computed tomography restoration
Computed Tomography (CT) is a widely use medical image modality in clinical medicine,
because it produces excellent visualizations of fine structural details of the human body. In …
because it produces excellent visualizations of fine structural details of the human body. In …
Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction
Commercial iterative reconstruction techniques help to reduce the radiation dose of
computed tomography (CT), but altered image appearance and artefacts can limit their …
computed tomography (CT), but altered image appearance and artefacts can limit their …
Multi-constraint generative adversarial network for dose prediction in radiotherapy
B Zhan, J Xiao, C Cao, X Peng, C Zu, J Zhou… - Medical Image …, 2022 - Elsevier
Radiation therapy (RT) is regarded as the primary treatment for cancer in the clinic, aiming to
deliver an accurate dose to the planning target volume (PTV) while protecting the …
deliver an accurate dose to the planning target volume (PTV) while protecting the …
Eformer: Edge enhancement based transformer for medical image denoising
In this work, we present Eformer-Edge enhancement based transformer, a novel architecture
that builds an encoder-decoder network using transformer blocks for medical image …
that builds an encoder-decoder network using transformer blocks for medical image …
Ultra-low-dose chest CT imaging of COVID-19 patients using a deep residual neural network
Objectives The current study aimed to design an ultra-low-dose CT examination protocol
using a deep learning approach suitable for clinical diagnosis of COVID-19 patients …
using a deep learning approach suitable for clinical diagnosis of COVID-19 patients …
Edcnn: Edge enhancement-based densely connected network with compound loss for low-dose ct denoising
In the past few decades, to reduce the risk of X-ray in computed tomography (CT), low-dose
CT image denoising has attracted extensive attention from researchers, which has become …
CT image denoising has attracted extensive attention from researchers, which has become …
Deep learning–based denoising of low-dose SPECT myocardial perfusion images: quantitative assessment and clinical performance
N Aghakhan Olia, A Kamali-Asl, S Hariri Tabrizi… - European journal of …, 2022 - Springer
Purpose This work was set out to investigate the feasibility of dose reduction in SPECT
myocardial perfusion imaging (MPI) without sacrificing diagnostic accuracy. A deep learning …
myocardial perfusion imaging (MPI) without sacrificing diagnostic accuracy. A deep learning …
[HTML][HTML] The use of deep learning towards dose optimization in low-dose computed tomography: A scoping review
E Immonen, J Wong, M Nieminen, L Kekkonen… - Radiography, 2022 - Elsevier
Introduction Low-dose computed tomography tends to produce lower image quality than
normal dose computed tomography (CT) although it can help to reduce radiation hazards of …
normal dose computed tomography (CT) although it can help to reduce radiation hazards of …
Deep cascade residual networks (DCRNs): optimizing an encoder–decoder convolutional neural network for low-dose CT imaging
To suppress noise and artifacts caused by the reduced radiation exposure in low-dose
computed tomography, several deep learning (DL)-based image restoration methods have …
computed tomography, several deep learning (DL)-based image restoration methods have …