CT image denoising and deblurring with deep learning: current status and perspectives
This article reviews the deep learning methods for computed tomography image denoising
and deblurring separately and simultaneously. Then, we discuss promising directions in this …
and deblurring separately and simultaneously. Then, we discuss promising directions in this …
Deep learning based unpaired image-to-image translation applications for medical physics: a systematic review
Purpose. There is a growing number of publications on the application of unpaired image-to-
image (I2I) translation in medical imaging. However, a systematic review covering the …
image (I2I) translation in medical imaging. However, a systematic review covering the …
MR image denoising and super-resolution using regularized reverse diffusion
Patient scans from MRI often suffer from noise, which hampers the diagnostic capability of
such images. As a method to mitigate such artifacts, denoising is largely studied both within …
such images. As a method to mitigate such artifacts, denoising is largely studied both within …
Restoration of motion-corrupted EEG signals using attention-guided operational CycleGAN
Electroencephalogram (EEG) signals suffer substantially from motion artifacts even in
ambulatory settings. Signal processing techniques for removing motion artifacts from EEG …
ambulatory settings. Signal processing techniques for removing motion artifacts from EEG …
TIME-Net: Transformer-integrated multi-encoder network for limited-angle artifact removal in dual-energy CBCT
Dual-energy cone-beam computed tomography (DE-CBCT) is a promising imaging
technique with foreseeable clinical applications. DE-CBCT images acquired with two …
technique with foreseeable clinical applications. DE-CBCT images acquired with two …
CycleGAN for undamaged-to-damaged domain translation for structural health monitoring and damage detection
The advances in data science in the last few decades have benefitted many other fields,
including Structural Health Monitoring (SHM). Artificial Intelligence (AI), such as Machine …
including Structural Health Monitoring (SHM). Artificial Intelligence (AI), such as Machine …
Speckle noise reduction for medical ultrasound images based on cycle-consistent generative adversarial network
J Liu, C Li, L Liu, H Chen, H Han, B Zhang… - … Signal Processing and …, 2023 - Elsevier
Medical ultrasound (US) images are corrupted by speckle noise, which can adversely affect
the disease diagnosis and treatment. Recently, the cycle-consistent adversarial network …
the disease diagnosis and treatment. Recently, the cycle-consistent adversarial network …
AIGAN: Attention–encoding Integrated Generative Adversarial Network for the reconstruction of low-dose CT and low-dose PET images
X-ray computed tomography (CT) and positron emission tomography (PET) are two of the
most commonly used medical imaging technologies for the evaluation of many diseases …
most commonly used medical imaging technologies for the evaluation of many diseases …
Triplet cross-fusion learning for unpaired image denoising in optical coherence tomography
Optical coherence tomography (OCT) is a widely-used modality in clinical imaging, which
suffers from the speckle noise inevitably. Deep learning has proven its superior capability in …
suffers from the speckle noise inevitably. Deep learning has proven its superior capability in …
Self-supervised deep learning for joint 3D low-dose PET/CT image denoising
Deep learning (DL)-based denoising of low-dose positron emission tomography (LDPET)
and low-dose computed tomography (LDCT) has been widely explored. However, previous …
and low-dose computed tomography (LDCT) has been widely explored. However, previous …