Low-dose CT image reconstruction using vector quantized convolutional autoencoder with perceptual loss
S Ramanathan, M Ramasundaram - Sādhanā, 2023 - Springer
Computed Tomography (CT) has become a useful screening procedure to identify disease
or injury within various regions of the human body. The human beings' health issues caused …
or injury within various regions of the human body. The human beings' health issues caused …
Generation model meets swin transformer for unsupervised low-dose CT reconstruction
Y Li, X Sun, S Wang, Y Qin, J Pan… - … Learning: Science and …, 2024 - iopscience.iop.org
Computed tomography (CT) has evolved into an indispensable tool for clinical diagnosis.
Reducing radiation dose crucially minimizes adverse effects but may introduce noise and …
Reducing radiation dose crucially minimizes adverse effects but may introduce noise and …
[HTML][HTML] Artificial intelligence-assisted multistrategy image enhancement of chest X-rays for COVID-19 classification
Background The coronavirus disease 2019 (COVID-19) led to a dramatic increase in the
number of cases of patients with pneumonia worldwide. In this study, we aimed to develop …
number of cases of patients with pneumonia worldwide. In this study, we aimed to develop …
[HTML][HTML] Progressive U-Net residual network for computed tomography images super-resolution in the screening of COVID-19
D Qiu, Y Cheng, X Wang - Journal of Radiation Research and Applied …, 2021 - Elsevier
Thin-slice computed tomography (CT) examination plays an important role in the screening
of suspected and confirmed coronavirus disease 2019 (COVID-19) outbreak patients …
of suspected and confirmed coronavirus disease 2019 (COVID-19) outbreak patients …
Interactive smoothing parameter optimization in dbt reconstruction using deep learning
Medical image reconstruction algorithms such as Penalized Weighted Least Squares
(PWLS) typically rely on a good choice of tuning parameters such as the number of …
(PWLS) typically rely on a good choice of tuning parameters such as the number of …
A deep learning reconstruction framework for X-ray computed tomography with incomplete data
As a powerful imaging tool, X-ray computed tomography (CT) allows us to investigate the
inner structures of specimens in a quantitative and nondestructive way. Limited by the …
inner structures of specimens in a quantitative and nondestructive way. Limited by the …
Weakly-supervised network for detection of COVID-19 in chest CT scans
Deep Learning-based chest Computed Tomography (CT) analysis has been proven to be
effective and efficient for COVID-19 diagnosis. Existing deep learning approaches heavily …
effective and efficient for COVID-19 diagnosis. Existing deep learning approaches heavily …
2.5 D deep learning for CT image reconstruction using a multi-GPU implementation
While Model Based Iterative Reconstruction (MBIR) of CT scans has been shown to have
better image quality than Filtered Back Projection (FBP), its use has been limited by its high …
better image quality than Filtered Back Projection (FBP), its use has been limited by its high …
Double paths network with residual information distillation for improving lung CT image super resolution
Y Chen, Q Zheng, J Chen - Biomedical Signal Processing and Control, 2022 - Elsevier
Objective Medical image analysis is particularly important for doctors to differential diagnosis
of diseases. Due to the outbreak of COVID-19, how to diagnose COVID-19 accurately has …
of diseases. Due to the outbreak of COVID-19, how to diagnose COVID-19 accurately has …
Accessible, affordable and low-risk lungs health monitoring in covid-19: Deep cascade reconstruction from degraded lr-uldct
We present deep cascade reconstruction of degraded low-resolution ultra-low-dose
computed tomography (LR-ULDCT) chest images to restored and super-resolved (SR) …
computed tomography (LR-ULDCT) chest images to restored and super-resolved (SR) …